recodex-wiki/Rewritten-docs.md

# Introduction

Generally, there are many different ways and opinions on how to teach people something new. However, most people agree that a hands-on experience is one of the best ways to make the human brain remember a new skill. Learning must be entertaining and interactive, with fast and frequent feedback. Some kinds of knowledge are more suitable for this practical type of learning than others, and fortunately, programming is one of them.

University education system is one of the areas where this knowledge can be applied. In computer programming, there are several requirements such as the code being syntactically correct, efficient and easy to read, maintain and extend. Correctness and efficiency can be tested automatically to help teachers save time for their research, but reviewing bad design, bad coding habits and logical mistakes is really hard to automate and requires manpower.

Checking programs written by students takes a lot of time and requires a lot of mechanical, repetitive work. The first idea of an automatic evaluation system comes from Stanford University professors in 1965. They implemented a system which evaluated code in Algol submitted on punch cards. In following years, many similar products were written.

There are two basic ways of automatically evaluating code -- statically (check the code without running it; safe, but not very precise) or dynamically (run the code on testing inputs with checking the outputs against reference ones; needs sandboxing, but provides good real world experience).

This project focuses on the machine-controlled part of source code evaluation. First, general concepts of grading systems are observed, new requirements are specified and project with similar functionality are examined. Also, problems of the software previously used at Charles University in Prague are briefly discussed. With acquired knowledge from such projects in production, we set up goals for the new evaluation system, designed the architecture and implemented a fully operational solution. The system is now ready for production testing at the university.

Assignment

The major goal of this project is to create a grading application that will be used for programming classes at the Faculty of Mathematics and Physics of the Charles University in Prague. However, the application should be designed in a modular fashion to be easily extended or even modified to make other ways of usage possible.

The system should be capable of dynamic analysis of programming code. It means, that following four basic steps have to be supported:

1. compile the code and check for compilation errors
2. run compiled binary in a sandbox with predefined inputs
3. check constraints on used amount of memory and time
4. compare program outputs with predefined values

The project has a great starting point -- there is an old grading system currently used at the university (CodEx), so its flaws and weaknesses can be addressed. Furthermore, many teachers desire to use and test the new system and they are willing to consult ideas or problems during development with us.

Intended usage

The whole system is intended to help both teachers (supervisors) and students. To achieve this, it is crucial to keep in mind typical usage scenarios of the system and try to make these tasks as simple as possible.

The system has a database of users. Each user has assigned a role, which corresponds to his/her privileges. There are user groups reflecting structure of lectured courses. Groups can be hierarchically ordered to reflect additional metadata such as the academic year. For example, a reasonable group hierarchy can look like this:

Summer term 2016
|-- Language C# and .NET platform
|   |-- Labs Monday 10:30
|   `-- Labs Thursday 9:00
|-- Programming I
|   |-- Labs Monday 14:00
 ...

In this example, students are members of the leaf groups, the higher level entities are just for keeping the related groups together. The hierarchy structure can be modified and altered to fit specific needs of the university or any other organization, even the flat structure (i.e., no hierarchy) is possible. One user can be part of multiple groups and on the other hand one group can have multiple users. Each user can have a specific role for every group in which is a member, overriding his/her default role in this context.

Database of exercises (algorithmic problems) is another part of the project. Each exercise consists of a text in multiple language variants, an evaluation configuration and a set of inputs and reference outputs. Exercises are created by instructed privileged users. Assigning an exercise to a group means to choose one of the available exercises and specifying additional properties. An assignment has a deadline (optionally a second deadline), a maximum amount of points, a configuration for calculating the final score, a maximum number of submissions, and a list of supported runtime environments (e.g., programming languages) including specific time and memory limits for each one.

Typical use cases for supported user roles are illustrated on following UML diagram:

Exercise evaluation chain

The most important part of the system is evaluation of solutions submitted by students. Concepts of consecutive steps from source code to final results is described in more detail below to give readers solid overview of what have to happen during evaluation process.

First thing users have to do is to submit their solutions through some user interface. Then, the system checks assignment invariants (deadlines, count of submissions, ...) and stores submitted files. The runtime environment is automatically detected based on input files and suitable exercise configuration variant is chosen (one exercise can have multiple variants, for example C and Java languages). Matching exercise configuration is then used for taking care of evaluation process.

There is a pool of worker computers dedicated to processing jobs. Some of them may have different environment to allow testing programs in more conditions. Incoming jobs are scheduled to particular worker depending on its capabilities and job requirements.

Job processing itself stars with obtaining source files and job configuration. The configuration is parsed into small tasks with simple piece of work. Evaluation itself goes in direction of tasks ordering. It is crucial to keep executive computer secure and stable, so isolated sandboxed environment is used when dealing with unknown source code. When the execution is finished, results are saved.

Results from worker contains only output data from processed tasks (this could be return value, consumed time, ...). On top of that, one value is calculated to express overall quality of the tested job. It is used as points for final student grading. Calculation method of this value may be different for each assignment. Data presented back to users include overview of job parts (which succeeded and which failed, optionally with reason like "memory limit exceeded") and achieved score (amount of awarded points).

Requirements

There are bunch of different formal requirements for the system. Some of them are necessary for any system for source code evaluation, some of them are specific for university deployment and some of them arose during the ten year long lifetime of the old system. There are not many ways to improve CodEx experience from the perspective of a student, but a lot of feature requests come from administrators and supervisors. The ideas were gathered mostly from our personal experience with the system and from meetings with faculty staff involved with the current system.

For clear arrangement all the requirements and wishes are presented grouped by categories.

System features

System features represents directly accessible functionality to users of the system. They describe the evaluation system in general and also university addons (mostly administrative features).

Pure user requirements

• users have their own account in the system
• system users can be members of multiple groups (reflecting courses or labs)
• there is a database of exercises; teachers can create exercises including textual description, sample inputs and correct reference outputs (for example "sum all numbers from given file and write the result to the standard output")
• there is a list of assigned exercises in each group and interface to submit a solution; teachers can assign an existing exercise to their class with some specific properties set (deadlines, etc.)
• user can see a list of submitted solutions for each assignment with corresponding results
• teachers can specify way of computation grading points which will be awarded to the students depending on the quality of his/her solution for each assignment extra
• teachers can view detailed data about their students (users of a their groups) including all submitted solutions; also, each of the solution can be manually reviewed, commented and assigned additional points (positive or negative)
• one particular solution can be marked as accepted (used for grading this assignment); otherwise, the solution with most points is used
• teacher can edit student solution and privately resubmit it; optionally saving all results (including temporary ones)
• localization of all texts (UI and exercises)
• Markdown support for creating exercise texts
• tagging exercises in database and search by these tags
• comments, comments, comments (exercises, tests, solutions, ...)
• plagiarism detection

Administrative requirements

• users can use an intuitive user interface for interaction with the system, mainly for viewing assigned exercises, uploading their own solutions to the assignments, and viewing the results of the solutions after an automatic evaluation is finished; the two wanted interfaces are web and command-line based
• user privilege separation (at least two roles -- student and supervisor)
• logging in through a university authentication system (e.g. LDAP)
• SIS (university information system) integration for fetching personal user data
• safe environment in which the students' solutions are executed
• support for multiple programming environments at once to avoid unacceptable workload for administrator (maintain separate installation for every course) and high hardware occupation
• advanced low-level evaluation flow configuration with high-level abstraction layer for ordinary configuration cases
• use of modern technologies with state-of-the-art compilers

Technical details

Technical details are requirements of technical character with no direct mapping to visible parts of system. In ideal word, users should not know about these if they work properly, but would be at least annoyed if these requirements were not met. Most notably they are these ones:

• user interface of the system accessible on users' computers without installation of any kind of additional software
• easy implementation of different user interfaces
• be ready for workload hundreds of students and tens of supervisors
• automated installation of all components
• source code with permissive license allowing further development; this also applies on used libraries and frameworks
• multi-platform worker supporting at least two major operating systems

Conclusion

The survey shows that there are a lot of different requirements and wishes for the new system. When the system is ready it is likely that there will be new ideas of how to use the system and thus the system must be designed to be easily extendable, so everyone can develop their own feature. This also means that widely used programming languages and techniques should be used, so users can quickly understand the code and make changes.

To find out the current state in the field of automatic grading systems we did a short market survey on the field of automatic grading systems at universities, programming contests, and possibly other places where similar tools are available.

Related work

This is not a complete list of available evaluators, but only a few projects which are used these days and can be an inspiration for our project. Each project from the list has a brief description and some key features mentioned.

CodEx

Currently used grading solution at the Faculty of Mathematics and Physics of the Charles University in Prague which was implemented in 2006 by a group of students. It is called CodEx -- The Code Examiner and it has been used with some improvements since then. The original plan was to use the system only for basic programming courses, but there was a demand for adapting it for many different subjects.

CodEx is based on dynamic analysis. It features a web-based interface, where supervisors can assign exercises to their students and the students have a time window to submit their solutions. Each solution is compiled and run in sandbox (MO-Eval). The metrics which are checked are: correctness of the output, time and memory limits. It supports programs written in C, C++, C#, Java, Pascal, Python and Haskell.

Current system is old, but robust. There were no major security incidents during its production usage. However, from today's perspective there are several drawbacks. The main ones are:

• web interface -- The web interface is simple and fully functional. But rapid development in web technologies opens new horizons of how web interface can be made.
• web API -- CodEx offers a very limited XML API based on outdated technologies that is not sufficient for users who would like to create custom interfaces such as a command line tool or mobile application.
• sandboxing -- MO-Eval sandbox is based on principle of monitoring system calls and blocking the bad ones. This can be easily done for single-threaded applications, but proves difficult with multi-threaded ones. In present day, parallelism is a very important area of computing, so there is requirement to test multi-threaded applications too.
• instances -- Different ways of CodEx usage scenarios requires separate instances (Programming I and II, Java, C#, etc.). This configuration is not user friendly (students have to register in each instance separately) and burdens administrators with unnecessary work. CodEx architecture does not allow sharing hardware between instances, which results in an inefficient use of hardware for evaluation.
• task extensibility -- There is a need to test and evaluate complicated programs for classes such as Parallel programming or Compiler principles, which have a more difficult evaluation chain than simple compilation/execution/evaluation provided by CodEx.

Progtest

Progtest is private project of FIT ČVUT in Prague. As far as we know it is used for C/C++, Bash programming and knowledge-based quizzes. There are several bonus points and penalties and also a few hints what is failing in the submitted solution. It is very strict on source code quality, for example -pedantic option of GCC, Valgrind for memory leaks or array boundaries checks via mudflap library.

Codility

Codility is a web based solution primary targeted to company recruiters. It is a commercial product available as a SaaS and it supports 16 programming languages. The UI of Codility is opensource, the rest of source code is not available. One interesting feature is 'task timeline' -- captured progress of writing code for each user.

CMS

CMS is an opensource distributed system for running and organizing programming contests. It is written in Python and contains several modules. CMS supports C/C++, Pascal, Python, PHP, and Java programming languages. PostgreSQL is a single point of failure, all modules heavily depend on the database connection. Task evaluation can be only a three step pipeline -- compilation, execution, evaluation. Execution is performed in Isolate, sandbox written by the consultant of our project, Mgr. Martin Mareš, Ph.D.

MOE

MOE is a grading system written in Shell scripts, C and Python. It does not provide a default GUI interface, all actions have to be performed from command line. The system does not evaluate submissions in real time, results are computed in batch mode after exercise deadline, using Isolate for sandboxing. Parts of MOE are used in other systems like CodEx or CMS, but the system is generally obsolete.

Kattis

Kattis is another SaaS solution. It provides a clean and functional web UI, but the rest of the application is too simple. A nice feature is the usage of a standardized format for exercises. Kattis is primarily used by programming contest organizers, company recruiters and also some universities.

Analysis

None of the existing projects we came across is capable of all the required features for the new system. There is no grading system which is designed to support a complicated evaluation pipeline, so this unexplored field has to be designed with caution. Also, no project is modern and extensible enough so it could be used as a base for the new system. After considering all these facts, it is clear that a new system has to be written from scratch. This implies, that only a subset of all the features will be implemented in the first version, the others coming in the following releases.

Gathered features are categorized based on priorities for the whole system. The highest priority has main functionality similar to current CodEx. It is a base line to be useful in production environment, but a new design allows to easily develop further. On top of that, most of ideas from faculty staff belongs to second priority bucket, which will be implemented as part of the project. The most complicated tasks from this category are advanced low-level evaluation configuration format, using modern tools, connecting to a university systems and merging separate system instances into single one. Other tasks are scheduled for next releases after successful project defense. Namely, these are high-level exercise evaluation configuration with user-friendly interface for common exercise types, SIS integration (when some API will be available from their side) and command-line submit tool. Plagiarism detection is not likely to be part of any release in near future unless someone other makes the engine. The detection problem is too hard to be solved as part of this project.

We named the new project as ReCodEx -- ReCodEx Code Examiner. The name should point to the old CodEx, but also reflect the new approach to solve issues. Re as part of the name means redesigned, rewritten, renewed, or restarted.

At this point there is a clear idea how the new system will be used and what are the major enhancements for future releases. With this in mind, the overall architecture can be sketched. From the previous research, several goals are set up for the new project. They mostly reflect drawbacks of the current version of CodEx and some reasonable wishes of university users. Most notable features are following:

• modern HTML5 web frontend written in JavaScript using a suitable framework
• REST API implemented in PHP, communicating with database, evaluation backend and a file server
• evaluation backend implemented as a distributed system on top of a message queue framework (ZeroMQ) with master-worker architecture
• multi-platform worker supporting Linux and Windows environment (latter without sandbox, no general purpose suitable tool available yet)
• evaluation procedure configured in a YAML file, compound of small tasks connected into an arbitrary oriented acyclic graph

The reasons supporting these decisions are explained in the rest of analysis chapter. Also a lot of smaller design choices are mentioned including possible options, what is picked to implement and why. But first, discuss basic concepts of the system.

Basic concepts

The system is designed as a web application. The requirements say, that user interface must be accessible from students' computers without installation additional software. This immediately implies that users are connected to the internet, so it is used as communication medium. Today, there are two main ways of designing graphical user interface -- native app or web page. Make nice and multi-platform application with graphical interface is almost impossible because of large number of different environments. Also, these applications often requires installation or at least downloading its files (sources or binaries). However, creating web application is easier, because every personal computer has a internet browser installed. Also, browsers supports sort of unified and standardized environment of HTML5 and JavaScript. CodEx is also web application and everybody seems happy about it. There are other communicating channels every user have available like email or git, but they are totally inappropriate for designing user interfaces on top of them.

The application interacts with users. From the project assignment is clear, that the system has to keep personalized data about users and adapt presented content according to this knowledge. User data cannot be publicly visible, so that implies necessity of user authentication. The application also has to support multiple ways of authentication (university authentication systems, a company LDAP server, an OAuth server...) and permit adding more security measures in the future, such as two-factor authentication.

User data also includes a privilege level. From the assignment it is required to have at least two roles, student and supervisor. However, it is wise to add administrator level, which takes care of the system as a whole and is responsible for core setup, monitoring, updates and so on. Student role has the least power, basically can just view assignments and submit solutions. Supervisors have more authority, so they can create exercises and assignments, view results of students etc. From the university organization, one possible level could be introduced, course guarantor. However, from real experience all duties related with lecturing of labs are already associated with supervisors, so this role seems not so useful. In addition, no one requested more than three level privilege scheme.

School labs are lessons for some students lead by one (or sometimes two or three) supervisors. Students have the same homework and supervisors are evaluating its solutions. This organization has to be carried into the new system. Counterpart to real labs are virtual groups. This concept was already discussed in previous chapter including need for hierarchical structure of groups. Right for attending labs has only a person, who is student of the university and is recorded in university information system. To allow restriction of group members in ReCodEx, there two type of groups -- public and private. Public groups are open for every registered users, but to become a member of private group one of its supervisors have to add that user. This could be done automatically at beginning of the term with data from information system, but unfortunately there is no such API yet. However, creating this API is now considered by university leadership. Another just as good solution for restricting membership of a group is to allow anyone join the group with supplementary confirmation of supervisors. It has no additional benefits, so approach with public and private groups is implemented.

Supervisors using CodEx in their labs usually set minimum amount of points required to get a credit. These points can be get by solving assigned exercises. To visually show users if they already have enough points, ReCodEx groups supports setting this limit. There are two equal ways how to set a limit -- absolute value or relative value to maximum. The latter way seems nicer, so it is implemented. The relative value is set in percents and is called threshold.

Our university has a few partner grammar schools. There were an idea, that they could use CodEx for teaching informatics classes. To make the setup simple for them, all the software and hardware would be provided by university and hosted in their datacentre. However, CodEx were not prepared to support this kind of usage and no one had time to manage a separate instance. With ReCodEx it is possible to offer hosted environment as a service to other subjects. The concept we figured out is based on user and group separation inside the system. There are multiple instances in the system, which means unit of separation. Each instance has own set of users and groups, exercises can be optionally shared. Evaluation backend is common for all instances. To keep track of active instances and paying customers, each instance must have a valid license to allow users submit their solutions. License is granted for defined period of time and can be revoked in advance if the subject do not keep approved terms and conditions.

The main work for the system is to evaluate programming exercises. The exercise is quite similar to homework assignment during school labs. When a homework is assigned, two things are important to know for users:

• description of the problem
• metadata -- when and whom to submit solutions, grading scale, penalties, etc.

To reflect this idea teachers and students are already familiar with, we decided to keep separation between problem itself (exercise) and its assignment. Exercise only describes one problem and provides testing data with description of how to evaluate it. In fact, it is template for assignments. Assignment then contains data from its exercise and additional metadata, which can be different for every assignment of the same exercise. This separation is natural for all users, in CodEx it is implemented in similar way and no other considerable solution was found.

Forgotten password

With authentication and some sort of dealing with passwords is related a problem with forgotten credentials, especially passwords. People easily forget them and there has to be some kind of mechanism to retrieve a new password or change the old one. Problem is that it cannot be done in totally secure way, but we can at least come quite close to it. First, there are absolutely not secure and recommendable ways how to handle that, for example sending the old password through email. A better, but still not secure solution is to generate a new one and again send it through email. This solution was provided in CodEx, users had to write an email to administrator, who generated a new password and sent it back to the sender. This simple solution could be also automated, but administrator had quite a big control over whole process. This might come in handy if there could be some additional checkups for example, but on the other hand it can be quite time consuming.

Probably the best solution which is often used and is fairly secure is following. Let us consider only case in which all users have to fill their email addresses into the system and these addresses are safely in the hands of the right users. When user finds out that he/she does not remember a password, he/she requests a password reset and fill in his/her unique identifier; it might be email or unique nickname. Based on matched user account the system generates unique access token and sends it to user via email address. This token should be time limited and usable only once, so it cannot be misused. User then takes the token or URL address which is provided in the email and go to the system's appropriate section, where new password can be set. After that user can sign in with his/her new password. As previously stated, this solution is quite safe and user can handle it on its own, so administrator does not have to worry about it. That is the main reason why this approach was chosen to be used.

Evaluation unit executed by ReCodEx

One of the bigger requests for the new system is to support a complex configuration of execution pipeline. The idea comes from lecturers of Compiler principles class who want to migrate their semi-manual evaluation process to CodEx. Unfortunately, CodEx is not capable of such complicated exercise setup. None of evaluation systems we found can handle such task, so design from scratch is needed.

There are two main approaches to design a complex execution configuration. It can be composed of small amount of relatively big components or much more small tasks. Big components are easy to write and whole configuration is reasonably small. The components are designed for current problems, so it is not scalable enough for pleasant future usage. This can be solved by introducing small set of single-purposed tasks which can be composed together. The whole configuration is then quite bigger, but with great adaptation ability for new conditions and also less amount of work programming them. For better user experience, configuration generators for some common cases can be introduced.

ReCodEx target is to be continuously developed and used for many years, so the smaller tasks are the right choice. Observation of CodEx system shows that only a few tasks are needed. In extreme case, only one task is enough -- execute a binary. However, for better portability of configurations along different systems it is better to implement reasonable subset of operations directly without calling system provided binaries. These operations are copy file, create new directory, extract archive and so on, altogether called internal tasks. Another benefit from custom implementation of these tasks is guarantied safety, so no sandbox needs to be used as in external tasks case.

For a job evaluation, the tasks needs to be executed sequentially in a specified order. The idea of running independent tasks in parallel is bad because exact time measurement needs controlled environment on target computer with minimization of interrupts by other processes. It seems that connecting tasks into directed acyclic graph (DAG) can handle all possible problem cases. None of the authors, supervisors and involved faculty staff can think of a problem that cannot be decomposed into tasks connected in a DAG. The goal of evaluation is to satisfy as many tasks as possible. During execution there are sometimes multiple choices of next task. To control that, each task can have a priority, which is used as a secondary ordering criterion. For better understanding, here is a small example.

The job root task is imaginary single starting point of each job. When the CompileA task is finished, the RunAA task is started (or RunAB, but should be deterministic by position in configuration file -- tasks stated earlier should be executed earlier). The task priorities guaranties, that after CompileA task all dependent tasks are executed before CompileB task (they have higher priority number). For example this is useful to control which files are present in a working directory at every moment. To sum up, there are 3 ordering criteria: dependencies, then priorities and finally position of task in configuration. Together, they define a unambiguous linear ordering of all tasks.

For grading there are several important tasks. First, tasks executing submitted code need to be checked for time and memory limits. Second, outputs of judging tasks need to be checked for correctness (represented by return value or by data on standard output) and should not fail on time or memory limits. This division can be transparent for backend, each task is executed the same way. But frontend must know which tasks from whole job are important and what is their kind. It is reasonable, to keep this piece of information alongside the tasks in job configuration, so each task can have a label about its purpose. Unlabeled tasks have an internal type inner. There are four categories of tasks:

• initiation -- setting up the environment, compiling code, etc.; for users failure means error in their sources which are not compatible with running it with examination data
• execution -- running the user code with examination data, must not exceed time and memory limits; for users failure means wrong design, slow data structures, etc.
• evaluation -- comparing user and examination outputs; for user failure means that the program does not compute the right results
• inner -- no special meaning for frontend, technical tasks for fetching and copying files, creating directories, etc.

Each job is composed of multiple tasks of these types which are semantically grouped into tests. A test can represent one set of examination data for user code. To mark the grouping, another task label can be used. Each test must have exactly one evaluation task (to show success or failure to users) and arbitrary number of tasks with other types.

Evaluation progress state

Users surely want to know progress state of their submitted solution this kind of functionality comes particularly handy in long duration exercises. Because of reporting progress users have immediate knowledge if anything goes wrong, not mention psychological effect that whole system and its parts are working and doing something. That is why this feature was considered from beginning but there are multiple ways how to look at it in particular.

The very first idea would be to provide progress state based on done messages from compilation, execution and evaluation. Which is something what a lot of evaluation systems are providing. These information are high level enough for users and they probably know what is going on and executing right now. If compilation fails users know that their solution is not compilable, if execution fails there were some problems with their program. The clarity of this kind of progress state is nice and understandable. But as we learnt ReCodEx has to have more advanced execution pipeline there can be more compilations or more executions. And in addition parts of the system which ensure execution of users solutions do not have to precisely know what they are executing at the moment. This kind of information may be meaningless for them.

That is why another solution of progress state was considered. As we know right now one of the best ways how to ensure generality is to have jobs with single-purpose tasks. These tasks can be anything, some internal operation or execution of external and sandboxed program. Based on this there is one very simple solution how to provide general progress state which should be independent on task types. We know that job has some number of tasks which has to be executed so we can send state info after execution of every task. And that is how we get percentual completion of an execution. Yes, it is kind of boring and standard way but on top of that there can be built something else and more appealing to users.

So displaying progress to users can be done numerous ways. We have percentual completion which is of course begging for simple solution which is displaying only the percentage or some kind of standard graphical progress bar. But that is too mainstream lets try something else. Very good idea is to have some kind of puzzled image or images which will be composed together according to progress. Nice way but kind of challenging if we do not have designer around. Another original solution is to have database of random kind-of-funny statements which will be displayed every time task is completed. It is easy enough for implementation and even for making up these messages and it is quite new and original. That is why this last solution was chosen for displaying progress state.

Results of evaluation

There are lot of things which deserves discussion concerning results of evaluation, how they should be displayed, what should be visible or not and also what kind of reward for users solutions should be chosen.

At first let us focus on all kinds of outputs from executed programs within job. Out of discussion is that supervisors should be able to view almost all outputs from solutions if they choose them to be visible and recorded. This feature is critical in debugging either whole exercises or users solutions. But should it be default behaviour to record every output? Absolutely not, supervisor should have a choice to turn it on, but discarding the outputs has to be the default option. Even without this functionality a file base around whole ReCodEx system can become quite large and on top of that outputs from executed programs can be sometimes very extensive. Storing this amount of data is inefficient and unnecessary to most of the solutions. However, on supervisor request this feature should be available.

More interesting question is what should regular users see from execution of their solution. Simple answer is of course that they should not see anything which is partly true. Outputs from their programs can be anything and users can somehow analyze inputs or even redirect them to output. So outputs from execution should not be visible at all or under very special circumstances. But that is not so straightforward for compilation or other kinds of initiation, where it really depends on the particular case. Generally it is quite harmless to display user some kind of compilation error which can help a lot during troubleshooting. Of course again this kind of functionality should be configurable by supervisors and disabled by default. There is also the last kind of tasks which can output some information which is evaluation tasks. Output of these tasks is somehow important to whole system and again can contain some information about inputs or reference outputs. So outputs of evaluation tasks should not be visible to regular users too.

The overall concept of grading solutions was presented earlier. To briefly remind that, backend returns only exact measured values (used time and memory, return code of the judging task, ...) and on top of that one value is computed. The way of this computation can be very different across supervisors, so it has to be easily extendable. The best way is to provide interface, which can be implemented and any sort of magic can return the final value.

We found out several computational possibilities. There is basic arithmetic, weighted arithmetic, geometric and harmonic mean of results of each test (the result is logical value succeeded/failed, optionally with weight), some kind of interpolation of used amount of time for each test, the same with used memory amount and surely many others. To keep the project simple, we decided to design appropriate interface and implement only weighted arithmetic mean computation, which is used in about 90% of all assignments. Of course, different scheme can be chosen for every assignment and also can be configured -- for example specifying test weights for implemented weighted arithmetic mean. Advanced ways of computation can be implemented on demand when there is a real demand for them.

To avoid assigning points for insufficient solutions (like only printing "File error" which is the valid answer in two tests), a minimal point threshold can be specified. It the solution is to get less points than specified, it will get zero points instead. This functionality can be embedded into grading computation algoritm itself, but it would have to be present in each implementation separately, which is a bit ugly. So, this feature is separated from point computation.

Automatic grading cannot reflect all aspects of submitted code. For example, structuring the code, number and quality of comments and so on. To allow supervisors bring these manually checked things into grading, there is a concept of bonus points. They can be positive or negative. Generally the solution with the most assigned points is marked for grading that particular assignment. However, if supervisor is not satisfied with student solution (really bad code, cheating, ...) he/she assigns the student negative bonus points. To prevent overriding this decision by system choosing another solution with more points or even student submitting the same code again which evaluates to more points, supervisor can mark a particular solution as marked and used for grading instead of solution with the most points.

Persistence

Previous parts of analysis show that the system has to keep some state. This could be user settings, group membership, evaluated assignments and so on. The data have to be kept across restart, so persistence is important decision factor. There are several ways how to save structured data:

• plain files
• NoSQL database
• relational database

Another important factor is amount and size of stored data. Our guess is about 1000 users, 100 exercises, 200 assignments per year and 400000 unique solutions per year. The data are mostly structured and there are a lot of them with the same format. For example, there is a thousand of users and each one has the same values -- name, email, age, etc. These kind of data are relatively small, name and email are short strings, age is an integer. Considering this, relational databases or formatted plain files (CSV for example) fits best for them. However, the data often have to support find operation, so they have to be sorted and allow random access for resolving cross references. Also, addition a deletion of entries should take reasonable time (at most logaritmic time complexity to number of saved values). This practically excludes plain files, so relational database is used instead.

On the other hand, there are some data with no such great structure and much larger size. These can be evaluation logs, sample input files for exercises or submitted sources by students. Saving this kind of data into relational database is not suitable, but it is better to keep them as ordinary files or store them into some kind of NoSQL database. Since they are already files and does not need to be backed up in multiple copies, it is easier to keep them as ordinary files in filesystem. Also, this solution is more lightweight and does not require additional dependencies on third-party software. File can be identified using its filesystem path or unique index stored as value in relational database. Both approaches are equally good, final decision depends on actual case.

Structure of the project

There are numerous ways how to divide some sort of system into separated services, from one single component to many and many single-purpose components. Having only one big service is not feasible, not scalable enough and mainly it would be one big blob of code which somehow works and is very complex, so this is not the way. The quite opposite, having a lot of single-purpose components is also somehow impractical. It is scalable by default and all services would have quite simple code but on the other hand communication requirements for such solution would be insane. So there has to be chosen approach which is somehow in the middle, that means services have to communicate in manner which will not bring network down, code basis should be reasonable and the whole system has to be scalable enough. With this being said there can be discussion over particular division for ReCodEx system.

The ReCodEx project is divided into two logical parts – the backend and the frontend – which interact which each other and which cover the whole area of code examination. Both of these logical parts are independent of each other in the sense of being installed on separate machines at different locations and that one of the parts can be replaced with a different implementation and as long as the communication protocols are preserved, the system will continue working as expected.

Backend is the part which is responsible solely for the process of evaluation a solution of an exercise. Each evaluation of a solution is referred to as a job. For each job, the system expects a configuration document of the job, supplementary files for the exercise (e.g., test inputs, expected outputs, predefined header files), and the solution of the exercise (typically source codes created by a student). There might be some specific requirements for the job, such as a specific runtime environment, specific version of a compiler or the job must be evaluated on a processor with a specific number of cores. The backend infrastructure decides whether it will accept a job or decline it based on the specified requirements. In case it accepts the job, it will be placed in a queue and it will be processed as soon as possible. The backend publishes the progress of processing of the queued jobs and the results of the evaluations can be queried after the job processing is finished. The backend produces a log of the evaluation and scores the solution based on the job configuration document.

From the scalable point of view there are two necessary components, the one which will execute jobs and component which will distribute jobs to the instances of the first one. This ensures scalability in manner of parallel execution of numerous jobs which is exactly what is needed. Implementation of these services are called broker and worker, first one handles distribution, latter execution. These components should be enough to fulfill all above said, but for the sake of simplicity and better communication gateways with frontend two other components were added, fileserver and monitor. Fileserver is simple component whose purpose is to store files which are exchanged between frontend and backend. Monitor is also quite simple service which is able to serve job progress state from worker to web application. These two additional services are on the edge of frontend and backend (like gateways) but logically they are more connected with backend, so it is considered they belong there.

Frontend on the other hand is responsible for the communication with the users and provides them a convenient access to the backend infrastructure. The frontend manages user accounts and gathers them into units called groups. There is a database of exercises which can be assigned to the groups and the users of these groups can submit their solutions for these assignments. The frontend will initiate evaluation of these solutions by the backend and it will store the results afterwards. The results will be visible to authorized users and the results will be awarded with points according to the score given by the backend in the evaluation process. The supervisors of the groups can edit the parameters of the assignments, review the solutions and the evaluations in detail and award the solutions with bonus points (both positive and negative) and discuss about the solution with the author of the solution. Some of the users can be entitled to create new exercises and extend the database of exercises which can be assigned to the groups later on.

There are two main purposes of frontend -- holding the state of whole system (database of users, exercises, solutions, points, etc.) and presenting the state to users through some kind of an user interface (e.g., a web application, mobile application, or a command-line tool). According to contemporary trends in development of frontend parts of applications, we decided to split the frontend in two logical parts -- a server side and a client side. The server side is responsible for managing the state and the client side gives instructions to the server side based on the inputs from the user. This decoupling gives us the ability to create multiple client side tools which may address different needs of the users.

The frontend developed as part of this project is a web application created with the needs of the Faculty of Mathematics and Physics of the Charles university in Prague in mind. The users are the students and their teachers, groups correspond to the different courses, the teachers are the supervisors of these groups. We believe that this model is applicable to the needs of other universities, schools, and IT companies, which can use the same system for their needs. It is also possible to develop their own frontend with their own user management system for their specific needs and use the possibilities of the backend without any changes, as was mentioned in the previous paragraphs.

One possible configuration of ReCodEx system is illustrated on following picture, where there is one shared backend with three workers and two separate instances of whole frontend. This configuration may be suitable for MFF UK -- basic programming course and KSP competition. But maybe even sharing web API and fileserver with only custom instances of client (web app or own implementation) is more likely to be used. Note, that connections between components are not fully accurate.

In the latter parts of the documentation, both of the backend and frontend parts will be introduced separately and covered in more detail. The communication protocol between these two logical parts will be described as well.

Implementation analysis

When developing a project like ReCodEx there has to be some discussion over implementation details and how to solve some particular problems properly. This discussion is a never ending story which is done through the whole development process. Some of the most important implementation problems or interesting observations will be discussed in this chapter.

General communication

Overall design of the project is discussed above. There are bunch of components with their own responsibility. Important thing to design is communication of these components. All we can count with is that they are connected by network.

To choose a suitable protocol, there are some additional requirements that should be met:

• reliability -- if a message is sent between components, the protocol has to ensure that it is received by target component
• working over IP protocol
• multi-platform and multi-language usage

TCP/IP protocol meets these conditions, however it is quite low level and working with it usually requires working with platform dependent non-object API. Often way to reflect these reproaches is to use some framework which provides better abstraction and more suitable API. We decided to go this way, so the following options are considered:

• CORBA -- Corba is a well known framework for remote object invocation. There are multiple implementations for almost every known programming language. It fits nicely into object oriented programming environment.
• RabbitMQ -- RabbitMQ is a messaging framework written in Erlang. It has bindings to huge number of languages and large community. Also, it is capable of routing requests, which could be handy feature for job loadbalancing.
• ZeroMQ -- ZeroMQ is another messaging framework, but instead of creating separate service this is a small library which can be embedded into own projects. It is written in C++ with huge number of bindings.

We like CORBA, but our system should be more loosely-coupled, so (asynchronous) messaging is better approach in our minds. RabbitMQ seems nice with great advantage of routing capability, but it is quite heavy service written in language no one from the team knows, so we do not like it much. ZeroMQ is the best option for us. However, all of the three options would have been possible to use.

Frontend communication follows the choice, that ReCodEx should be primary a web application. The communication protocol has to reflect client-server architecture. There are several options:

• TCP sockets -- TCP sockets give a reliable means of a full-duplex communication. All major operating systems support this protocol and there are libraries which simplify the implementation. On the other side, it is not possible to initiate a TCP socket from a web browser.
• WebSockets -- The WebSocket standard is built on top of TCP. It enables a web browser to connect to a server over a TCP socket. WebSockets are implemented in recent versions of all modern web browsers and there are libraries for several programming languages like Python or JavaScript (running in Node.js). Encryption of the communication over a WebSocket is supported as a standard.
• HTTP protocol -- The HTTP protocol is a state-less protocol implemented on top of the TCP protocol. The communication between the client and server consists of a requests sent by the client and responses to these requests sent back by the sever. The client can send as many requests as needed and it may ignore the responses from the server, but the server must respond only to the requests of the client and it cannot initiate communication on its own. End-to-end encryption can be achieved easily using SSL (HTTPS).

We chose the HTTP(S) protocol because of the simple implementation in all sorts of operating systems and runtime environments on both the client and the server side.

The API of the server should expose basic CRUD (Create, Read, Update, Delete) operations. There are some options on what kind of messages to send over the HTTP:

• SOAP -- a protocol for exchanging XML messages. It is very robust and complex.
• REST -- is a stateless architecture style, not a protocol or a technology. It relies on HTTP (but not necessarily) and its method verbs (e.g., GET, POST, PUT, DELETE). It can fully implement the CRUD operations.

Even though there are some other technologies we chose the REST style over the HTTP protocol. It is widely used, there are many tools available for development and testing, and it is understood by programmers so it should be easy for a new developer with some experience in client-side applications to get to know with the ReCodEx API and develop a client application.

To sum up, chosen ways of communication inside the ReCodEx system are captured in the following image. Red connections are through ZeroMQ sockets, blue are through WebSockets and green are through HTTP(S).

Broker

The broker is responsible for keeping track of available workers and distributing jobs that it receives from the frontend between them.

Worker management

It is intended for the broker to be a fixed part of the backend infrastructure to which workers connect at will. Thanks to this design, workers can be added and removed when necessary (and possibly in an automated fashion), without changing the configuration of the broker. An alternative solution would be configuring a list of workers before startup, thus making them passive in the communication (in the sense that they just wait for incoming jobs instead of connecting to the broker). However, this approach comes with a notable administration overhead -- in addition to starting a worker, the administrator would have to update the worker list.

Worker management must also take into account the possibility of worker disconnection, either because of a network or software failure (or termination). A common way to detect such events in distributed systems is to periodically send short messages to other nodes and expect a response. When these messages stop arriving, we presume that the other node encountered a failure. Both the broker and workers can be made responsible for initiating these exchanges and it seems that there are no differences stemming from this choice. We decided that the workers will be the active party that initiates the exchange.

Scheduling

Jobs should be scheduled in a way that ensures that they will be processed without unnecessary waiting. This depends on the fairness of the scheduling algorithm (no worker machine should be overloaded).

The design of such scheduling algorithm is complicated by the requirements on the diversity of workers -- they can differ in operating systems, available software, computing power and many other aspects.

We decided to keep the details of connected workers hidden from the frontend, which should lead to a better separation of responsibilities and flexibility. Therefore, the frontend needs a way of communicating its requirements on the machine that processes a job without knowing anything about the available workers. A key-value structure is suitable for representing such requirements.

With respect to these constraints, and because the analysis and design of a more sophisticated solution was declared out of scope of our project assignment, a rather simple scheduling algorithm was chosen. The broker shall maintain a queue of available workers. When assigning a job, it traverses this queue and chooses the first machine that matches the requirements of the job. This machine is then moved to the end of the queue.

Presented algorithm results in a simple round-robin load balancing strategy, which should be sufficient for small-scale deployments (such as a single university). However, with a large amount of jobs, some workers will easily become overloaded. The implementation must allow for a simple replacement of the load balancing strategy so that this problem can be solved in the near future.

Forwarding jobs

Information about a job can be divided in two disjoint parts -- what the worker needs to know to process it and what the broker needs to forward it to the correct worker. It remains to be decided how this information will be transferred to its destination.

It is technically possible to transfer all the data required by the worker at once through the broker. This package could contain submitted files, test data, requirements on the worker, etc. A drawback of this solution is that both submitted files and test data can be rather large. Furthermore, it is likely that test data would be transferred many times.

Because of these facts, we decided to store data required by the worker using a shared storage space and only send a link to this data through the broker. This approach leads to a more efficient network and resource utilization (the broker doesn't have to process data that it doesn't need), but also makes the job submission flow more complicated.

Further requirements

The broker can be viewed as a central point of the backend. While it has only two primary, closely related responsibilities, other requirements have arisen (forwarding messages about job evaluation progress back to the frontend) and will arise in the future. To facilitate such requirements, its architecture should allow simply adding new communication flows. It should also be as asynchronous as possible to enable efficient communication with external services, for example via HTTP.

Worker

Worker is component which is supposed to execute incoming jobs from broker. As such worker should work and support wide range of different infrastructures and maybe even platforms/operating systems. Support of at least two main operating systems is desirable and should be implemented. Worker as a service does not have to be much complicated, but a bit of complex behaviour is needed. Mentioned complexity is almost exclusively concerned about robust communication with broker which has to be regularly checked. Ping mechanism is usually used for this in all kind of projects. This means that worker should be able to send ping messages even during execution. So worker has to be divided into two separate parts, the one which will handle communication with broker and the another which will execute jobs. The easiest solution is to have these parts in separate threads which somehow tightly communicates with each other. For inter process communication there can be used numerous technologies, from shared memory to condition variables or some kind of in-process messages. Already used library ZeroMQ is possible to provide in-process messages working on the same principles as network communication which is quite handy and solves problems with threads synchronization and such.

At this point we have worker with two internal parts listening one and execution one. Implementation of first one is quite straightforward and clear. So lets discuss what should be happening in execution subsystem. Jobs as work units can quite vary and do completely different things, that means configuration and worker has to be prepared for this kind of generality. Configuration and its solution was already discussed above, implementation in worker is then quite also quite straightforward. Worker has internal structures to which loads and which stores metadata given in configuration. Whole job is mapped to job metadata structure and tasks are mapped to either external ones or internal ones (internal commands has to be defined within worker), both are different whether they are executed in sandbox or as internal worker commands.

Another division of tasks is by task-type field in configuration. This field can have four values: initiation, execution, evaluation and inner. All was discussed and described above in configuration analysis. What is important to worker is how to behave if execution of task with some particular type fails. There are two possible situations execution fails due to bad user solution or due to some internal error. If execution fails on internal error solution cannot be declared overly as failed. User should not be punished for bad configuration or some network error. This is where task types are useful. Generally initiation, execution and evaluation are tasks which are somehow executing code which was given by users who submitted solution of exercise. If this kinds of tasks fail it is probably connected with bad user solution and can be evaluated. But if some inner task fails solution should be re-executed, in best case scenario on different worker. That is why if inner task fails it is sent back to broker which will reassign job to another worker. More on this subject should be discussed in broker assigning algorithms section.

There is also question about working directory or directories of job, which directories should be used and what for. There is one simple answer on this every job will have only one specified directory which will contain every file with which worker will work in the scope of whole job execution. This is of course nonsense there has to be some logical division. The least which must be done are two folders one for internal temporary files and second one for evaluation. The directory for temporary files is enough to comprehend all kind of internal work with filesystem but only one directory for whole evaluation is somehow not enough. Users solutions are downloaded in form of zip archives so why these should be present during execution or why the results and files which should be uploaded back to fileserver should be cherry picked from the one big directory? The answer is of course another logical division into subfolders. The solution which was chosen at the end is to have folders for downloaded archive, decompressed solution, evaluation directory in which user solution is executed and then folders for temporary files and for results and generally files which should be uploaded back to fileserver with solution results. Of course there has to be hierarchy which separate folders from different workers on the same machines. That is why paths to directories are in format: ${DEFAULT}/${FOLDER}/${WORKER_ID}/${JOB_ID} where default means default working directory of whole worker, folder is particular directory for some purpose (archives, evaluation, ...). Mentioned division of job directories proved to be flexible and detailed enough, everything is in logical units and where it is supposed to be which means that searching through this system should be easy. In addition if solutions of users have access only to evaluation directory then they do not have access to unnecessary files which is better for overall security of whole ReCodEx.

As we discovered above worker has job directories but users who are writing and managing job configurations do not know where they are (on some particular worker) and how they can be accessed and written into configuration. For this kind of task we have to introduce some kind of marks or signs which will represent particular folders. Marks or signs can have form of some kind of special strings which can be called variables. These variables then can be used everywhere where filesystem paths are used within configuration file. This will solve problem with specific worker environment and specific hierarchy of directories. Final form of variables is ${...} where triple dot is textual description. This format was used because of special dollar sign character which cannot be used within filesystem path, braces are there only to border textual description of variable.

Evaluation

After successful arrival of job, worker has to prepare new execution environment, then solution archive has to be downloaded from fileserver and extracted. Job configuration is located within these files and loaded into internal structures and executed. After that results are uploaded back to fileserver. These steps are the basic ones which are really necessary for whole execution and have to be executed in this precise order.

Interesting problem is with supplementary files (inputs, sample outputs). There are two approaches which can be observed. Supplementary files can be downloaded either on the start of the execution or during execution. If the files are downloaded at the beginning execution does not really started at this point and if there are problems with network worker find it right away and can abort execution without executing single task. Slight problems can arise if some of the files needs to have same name (e.g. solution assumes that input is input.txt), in this scenario downloaded files cannot be renamed at the beginning but during execution which is somehow impractical and not easily observed. Second solution of this problem when files are downloaded on the fly has quite opposite problem, if there are problems with network worker will find it during execution when for instance almost whole execution is done, this is also not ideal solution if we care about burnt hardware resources. On the other hand using this approach users have quite advanced control of execution flow and know what files exactly are available during execution which is from users perspective probably more appealing then the first solution. Based on that downloading of supplementary files using 'fetch' tasks during execution was chosen and implemented.

Caching mechanism

Worker can use caching mechanism based on files from fileserver under one condition, provided files has to have unique name. If uniqueness is fulfilled then precious bandwidth can be saved using cache. This means there has to be system which can download file, store it in cache and after some time of inactivity delete it. Because there can be multiple worker instances on some particular server it is not efficient to have this system in every worker on its own. So it is feasible to have this feature somehow shared among all workers on the same machine. Solution may be again having separate service connected through network with workers which would provide such functionality but this would mean component with another communication for the purpose where it is not exactly needed. But mainly it would be single-failure component if it would stop working it is quite problem. So there was chosen another solution which assumes worker has access to specified cache folder, to this folder worker can download supplementary files and copy them from here. This means every worker has the possibility to maintain downloads to cache, but what is worker not able to properly do is deletion of unused files after some time. For that single-purpose component is introduced which is called 'cleaner'. It is simple script executed within cron which is able to delete files which were unused for some time. Together with worker fetching feature cleaner completes machine specific caching system.

Cleaner as mentioned is simple script which is executed regularly as cron job. If there is caching system like it was introduced in paragraph above there are little possibilities how cleaner should be implemented. On various filesystems there is usually support for two particular timestamps, last access time and last modification time. Files in cache are once downloaded and then just copied, this means that last modification time is set only once on creation of file and last access time should be set every time on copy. This imply last access time is what is needed here. But last modification time is widely used by operating systems, on the other hand last access time is not by default. More on this subject can be found here. For proper cleaner functionality filesystem which is used by worker for caching has to have last access time for files enabled.

Having cleaner as separated component and caching itself handled in worker is kind of blurry and is not clearly observable that it works without any race conditions. The goal here is not to have system without races but to have system which can recover from them. Implementation of caching system is based upon atomic operations of underlying filesystem. Follows description of one possible robust implementation. First start with worker implementation:

• worker discovers fetch task which should download supplementary file
• worker takes name of file and tries to copy it from cache folder to its working folder
  • if successful then last access time should be rewritten (by filesystem itself) and whole operation is done
  • if not successful then file has to be downloaded
    • file is downloaded from fileserver to working folder
    • downloaded file is then copied to cache

Previous implementation is only within worker, cleaner can anytime intervene and delete files. Implementation in cleaner follows:

• cleaner on its start stores current reference timestamp which will be used for comparison and load configuration values of caching folder and maximal file age
• there is a loop going through all files and even directories in specified cache folder
  • last access time of file or folder is detected
  • last access time is subtracted from reference timestamp into difference
  • difference is compared against specified maximal file age, if difference is greater, file or folder is deleted

Previous description implies that there is gap between detection of last access time and deleting file within cleaner. In the gap there can be worker which will access file and the file is anyway deleted but this is fine, file is deleted but worker has it copied. Another problem can be with two workers downloading the same file, but this is also not a problem file is firstly downloaded to working folder and after that copied to cache. And even if something else unexpectedly fails and because of that fetch task will fail during execution even that should be fine. Because fetch tasks should have 'inner' task type which implies that fail in this task will stop all execution and job will be reassigned to another worker. It should be like the last salvation in case everything else goes wrong.

Sandboxing

There are numerous ways how to approach sandboxing on different platforms, describing all possible approaches is out of scope of this document. Instead of that have a look at some of the features which are certainly needed for ReCodEx and propose some particular sandboxes implementations on Linux or Windows.

General purpose of sandbox is safely execute software in any form, from scripts to binaries. Various sandboxes differ in how safely are they and what limiting features they have. Ideal situation is that sandbox will have numerous options and corresponding features which will allow administrators to setup environment as they like and which will not allow user programs to somehow damage executing machine in any way possible.

For ReCodEx and its evaluation there is need for at least these features: execution time and memory limitation, disk operations limit, disk accessibility restrictions and network restrictions. All these features if combined and implemented well are giving pretty safe sandbox which can be used for all kinds of users solutions and should be able to restrict and stop any standard way of attacks or errors.

Linux systems have quite extent support of sandboxing in kernel, there were introduced and implemented kernel namespaces and cgroups which combined can limit hardware resources (cpu, memory) and separate executing program into its own namespace (pid, network). These two features comply sandbox requirement for ReCodEx so there were two options, either find existing solution or implement new one. Luckily existing solution was found and its name is isolate. Isolate does not use all possible kernel features but only subset which is still enough to be used by ReCodEx.

The opposite situation is in Windows world, there is limited support in its kernel which makes sandboxing a bit trickier. Windows kernel only has ways how to restrict privileges of a process through restriction of internal access tokens. Monitoring of hardware resources is not possible but used resources can be obtained through newly created job objects. But find sandbox which can do all things needed for ReCodEx seems to be impossible. There are numerous sandboxes for Windows but they all are focused on different things in a lot of cases they serves as safe environment for malicious programs, viruses in particular. Or they are designed as a separate filesystem namespace for installing a lot of temporarily used programs. From all these we can mention Sandboxie, Comodo Internet Security, Cuckoo sandbox and many others. None of these is fitted as sandbox solution for ReCodEx. With this being said we can safely state that designing and implementing new general sandbox for Windows is out of scope of this project.

New general sandbox for Windows is out of business but what about more specialized solution used for instance only for C#. CLR as a virtual machine and runtime environment has a pretty good security support for restrictions and separation which is also transferred to C#. This makes it quite easy to implement simple sandbox within C# but surprisingly there cannot be found some well known general purpose implementations. As said in previous paragraph implementing our own solution is out of scope of project there is simple not enough time. But C# sandbox is quite good topic for another project for example term project for C# course so it might be written and integrated in future.

Fileserver

The fileserver provides access to a shared storage space that contains files submitted by students, supplementary files such as test inputs and outputs and results of evaluation. In other words, it acts as an intermediate node for data passed between the frontend and the backend. This functionality can be easily separated from the rest of the backend features, which led to designing the fileserver as a standalone component. Such design helps encapsulate the details of how the files are stored (e.g. on a file system, in a database or using a cloud storage service), while also making it possible to share the storage between multiple ReCodEx frontends.

For early releases of the system, we chose to store all files on the file system -- it is the least complicated solution (in terms of implementation complexity) and the storage backend can be rather easily migrated to a different technology.

One of the facts we learned from CodEx is that many exercises share test input and output files, and also that these files can be rather large (hundreds of megabytes). A direct consequence of this is that we cannot add these files to submission archives that are to be downloaded by workers -- the combined size of the archives would quickly exceed gigabytes, which is impractical. Another conclusion we made is that a way to deal with duplicate files must be introduced.

A simple solution to this problem is storing supplementary files under the hashes of their content. This ensures that every file is stored only once. On the other hand, it makes it more difficult to understand what the content of a file is at a glance, which might prove problematic for the administrator.

A notable part of the fileserver's work is done by a web server (e.g. listening to HTTP requests and caching recently accessed files in memory for faster access). What remains to be implemented is handling requests that upload files -- student submissions should be stored in archives to facilitate simple downloading and supplementary exercise files need to be stored under their hashes.

We decided to use Python and the Flask web framework. This combination makes it possible to express the logic in ~100 SLOC and also provides means to run the fileserver as a standalone service (without a web server), which is useful for development.

Monitor

Users want to view real time evaluation progress of their solution. It can be easily done with established double-sided connection stream, but it is hard to achieve with web technologies. HTTP protocol works differently on separate requests basis with no long term connection. However, there is widely used technology to solve this problem, WebSocket protocol.

Working with WebSocket protocol from the backend is possible, but not ideal from design point of view. Backend should be hidden from public internet to minimize surface for possible attacks. With this in mind, there are two possible options:

• send progress messages through API
• make separate component for progress messages

Each of the two possibilities has some pros and cons. The first one is good because there is no additional component and API is already publicly visible. On the other side, working with WebSocket protocol from PHP is not much pleasant (but it is possible) and embedding this functionality into API is not extendable. The second approach is better for future changing the protocol or implementing extensions like caching of messages. Also, the progress feature is considered only optional, because there may be clients for which this feature is useless. Major drawback of separate component is another part, which needs to be publicly exposed.

We decided to make a separate component, mainly because it is smaller component with only one role, better maintainability and optional demands for progress callback.

There are several possibilities how to write the component. Notably, considered options were already used languages C++, PHP, JavaScript and Python. At the end, the Python language was chosen for its simplicity, great support for all used technologies and also there are free Python developers in out team. Then, responsibility of this component is determined. Concept of message flow is on following picture.

The message channel inputing the monitor uses ZeroMQ as main message framework used by backend. This decision keeps rest of backend aware of used communication protocol and related libraries. Output channel is WebSocket as a protocol for sending messages to web browsers. In Python, there are several WebSocket libraries. The most popular one is websockets in cooperation with asyncio. This combination is easy to use and well documented, so it is used in monitor component too. For ZeroMQ, there is zmq library with binding to framework core in C++.

Incoming messages are cached for short period of time. Early testing shows, that backend can start sending progress messages sooner than client connects to the monitor. To solve this, messages for each job are hold 5 minutes after reception of last message. The client gets all already received messages at time of connection with no message loss.

API server

The API server must handle HTTP requests and manage the state of the application in some kind of a database. It must also be able to communicate with the backend over ZeroMQ.

We considered several technologies which could be used:

• PHP + Apache -- one of the most widely used technologies for creating web servers. It is a suitable technology for this kind of a project. It has all the features we need when some additional extensions are installed (to support LDAP or ZeroMQ).
• Ruby on Rails, Python (Django), etc. -- popular web technologies that appeared in the last decade. Both support ZeroMQ and LDAP via extensions and have large developer communities.
• ASP.NET (C#), JSP (Java) -- these technologies are very robust and are used to create server technologies in many big enterprises. Both can run on Windows and Linux servers (ASP.NET using the .NET Core).
• JavaScript (Node.js) -- it is a quite new technology and it is being used to create REST APIs lately. Applications running on Node.js are quite performant and the number of open-source libraries available on the Internet is very huge.

We chose PHP and Apache mainly because we were familiar with these technologies and we were able to develop all the features we needed without learning to use a new technology. Since the number of features was quite high and needed to meet a strict deadline. This does not mean that we would find all the other technologies superior to PHP in all other aspects - PHP 7 is a mature language with a huge community and a wide range of tools, libraries, and frameworks.

We decided to use an ORM framework to manage the database, namely the widely used PHP ORM Doctrine 2. Using an ORM tool means we do not have to write SQL queries by hand. Instead, we work with persistent objects, which provides a higher level of abstraction. Doctrine also has a robust database abstraction layer so the database engine is not very important and it can be changed without any need for changing the code. MariaDB was chosen as the storage backend.

To speed up the development process of the PHP server application we decided to use a web framework. After evaluating and trying several frameworks, such as Lumen, Laravel, and Symfony, we ended up using Nette. This framework is very common in Czech Republic -- its lead developer is a well-known Czech programmer David Grudl -- and we were already familiar with the patterns used in this framework, such as dependency injection, authentication, routing. These concepts are useful even when developing a REST application, which might be a surprise considering that Nette focuses on "traditional" web applications. There is also a Nette extension which makes integartion of Doctrine 2 very straightforward.

Request handling

@todo Nette Router, how we exploit presenters to act as api endpoints

Authentication

Because Nette is focused on building web applications that render a new page for (almost) every request, it uses PHP sessions (based on cookies) for authentication. This method is unsuitable for REST APIs where clients do not typically store cookies. However, it is common that RESTful services provide access tokens that are then sent with every request by the client.

JWT (JSON web tokens), an open standard for access tokens, was chosen for our authentication implementation. Support libraries exist for all major languages used in web developments that facilitate straightforward usage. The tokens use asymmetric cryptography for signing, which provides a satisfactory level of security.

To implement JWT in Nette, we have to implement some of its security-related interfaces such as IAuthenticator and IUserStorage, which is rather easy thanks to the simple authentication flow. Replacing these services in a Nette application is also straightforward, thanks to its dependency injection container implementation.

Solution loading

When a solution evaluation on backend is finished, results are saved to fileserver and API is notified by broker about this state. Then some further steps needs to be done before the results can be presented to users. For example, these steps are parsing of the results, computating score or saving structured data into database. There are two main possibilities of loading:

• immediately
• on demand

They are almost equal, none of them provides any kind of big advantage. Loading solutions immediately is better, because fetching results from client for the first time can be a bit faster. On the other hand, loading them on demand when they are requested for the first time can save some resources when the solution results are not important and no one is interested in them.

From this choice, we picked up lazy loading when the results are requested. However, concept of asynchronous jobs is then introduced. This type of job is useful for batch submitting of jobs, for example rerunning jobs which failed on worker hardware issue. These jobs are typically submitted by different user than author (administator for example), so original authors should be notified. In this case it is more reasonable to load the results immediately and optionally send them with the notification. Exactly this is also done, special asynchronous jobs are loaded immediately with email notification to the original job author.

From a short time distance it seems that immediate loading of all jobs could simplify loading code and has no major drawbacks. In the next version of ReCodEx we will rethink this decission.

@todo: what files are stored in api, why there are duplicates among api and fileserver

@todo permission handling, roles, etc.

Backend management

Considering the fact that we have backend as a separate component which has no clue about administrators and uses only logging as some kind of failure reporting. It can be handy to provide this functionality to backend from frontend which manages users. The simplest solution would be again to have separate component with some sort of public interface. It can be for example REST or some other communication which backend can handle. Functionality of this kind of component is then quite easy. When request for report arrives from backend then type is inferred and if it is error which deserves attention of administrator then email is sent to him/her. There can also be errors which are not that important, was somehow solved by backend itself or are only informative, these do not have to be reported by email but only stored in persistent database for further consideration. On top of that separate component can be internal and not exposed to outside network. Disadvantage is that database layer which is used in some particular API instance cannot be used here because multiple instances of API can use one backend.

Another solution which was at the end implemented is to integrate backend failure reporting feature to API. Problem with previous one is that if job execution fails backend has to report this error to some particular API server from which request for evaluation came. This information is essential and has to be stored there and not in some general component and general error database. Obviously if there are multiple API servers connected to one backend there has to be some API server configured in backend as the main one which receives reports about general backend errors which are not connected to jobs. This solution was chosen because as stated we have to implement job error reporting in API and having separate component only for general errors is not feasible. In the end error reporting should be available under different route which is secured by basic HTTP authentication, because basic authentication is easy enough to implement in low-level backend components. That also means this feature is visible and can be exploited but from our points of view it seems as appropriate compromise in simplicity.

@todo: where is stored which workers can be used by supervisors and which runtimes are available, describe possibilities and why is not implemented automatic solution

Web-app

@todo: what technologies can be used on client frontend side, why react was used

@todo: please think about more stuff about api and web-app... thanks ;-)

# The Backend

The backend is the part which is hidden to the user and which has only one purpose: evaluate user’s solutions of their assignments.

@todo: describe the configuration inputs of the Backend

@todo: describe the outputs of the Backend

@todo: describe how the backend receives the inputs and how it communicates the results

Components

Whole backend is not just one service/component, it is quite complex system on its own.

@todo: describe the inner parts of the Backend (and refer to the Wiki for the technical description of the components)

Broker

@todo: gets stuff done, single point of failure and center point of ReCodEx universe

Fileserver

@todo: stores particular data from frontend and backend, hashing, HTTP API

Worker

@todo: describe a bit of internal structure in general

@todo: describe how jobs are generally executed

Monitor

@todo: not necessary component which can be omitted, proxy-like service

Backend internal communication

@todo: internal backend communication, what communicates with what and why

The Frontend

The frontend is the part which is visible to the user of ReCodEx and which holds the state of the system – the user accounts, their roles in the system, the database of exercises, the assignments of these exercises to groups of users (i.e., students), and the solutions and evaluations of them.

Frontend is split into three parts:

• the server-side REST API (“API”) which holds the business logic and keeps the state of the system consistent

• the relational database (“DB”) which persists the state of the system

• the client side application (“client”) which simplifies access to the API for the common users

The centerpiece of this architecture is the API. This component receives requests from the users and from the Backend, validates them and modifies the state of the system and persists this modified state in the DB.

We have created a web application which can communicate with the API server and present the information received from the server to the user in a convenient way. The client can be though any application, which can send HTTP requests and receive the HTTP responses. Users can use general applications like cURL, Postman, or create their own specific client for ReCodEx API.

Frontend capabilities

@todo: describe what the frontend is capable of and how it really works, what are the limitations and how it can be extended

Terminology

This project was created for the needs of a university and this fact is reflected into the terminology used throughout the Frontend. A list of important terms’ definitions follows to make the meaning unambiguous.

User and user roles

User is a person who uses the application. User is granted access to the application once he or she creates an account directly through the API or the web application. There are several types of user accounts depending on the set of permissions – a so called “role” – they have been granted. Each user receives only the most basic set of permissions after he or she creates an account and this role can be changed only by the administrators of the service:

• Student is the most basic role. Student can become member of a group and submit his solutions to his assignments.

• Supervisor can be entitled to manage a group of students. Supervisor can assign exercises to the students who are members of his groups and review their solutions submitted to these assignments.

• Super-admin is a user with unlimited rights. This user can perform any action in the system.

There are two implicit changes of roles:

• Once a student is added to a group as its supervisor, his role is upgraded to a supervisor role.

• Once a supervisor is removed from the lasts group where he is a supervisor then his role is downgraded to a student role.

These mechanisms do not prevent a single user being a supervisor of one group and student of a different group as supervisors’ permissions are superset of students’ permissions.

Login

Login is a set of user’s credentials he must submit to verify he can be allowed to access the system as a specific user. We distinguish two types of logins: local and external.

• Local login is user’s email address and a password he chooses during registration.

• External login is a mapping of a user profile to an account of some authentication service (e.g., CAS).

Instance

An instance of ReCodEx is in fact just a set of groups and user accounts. An instance should correspond to a real entity as a university, a high-school, an IT company or an HR agency. This approach enables the system to be shared by multiple independent organizations without interfering with each other.

Usage of the system by the users of an instance can be limited by possessing a valid license. It is up to the administrators of the system to determine the conditions under which they will assign licenses to the instances.

Group

Group corresponds to a school class or some other unit which gathers users who will be assigned the same set exercises. Each group can have multiple supervisors who can manage the students and the list of assignments.

Groups can form a tree hierarchy of arbitrary depth. This is inspired by the hierarchy of school classes belonging to the same subject over several school years. For example, there can be a top level group for a programming class that contains subgroups for every school year. These groups can then by divided into actual student groups with respect to lab attendance. Supervisors can create subgroups of their groups and further manage these subgroups.

Exercise

An exercise consists of textual assignment of a task and a definition of how a solution to this exercise should be processed and evaluated in a specific runtime environment (i.e., how to compile a submitted source code and how to test the correctness of the program). It is a template which can be instantiated as an assignment by a supervisor of a group.

Assignment

An assignment is an instance of an exercise assigned to a specific group. An assignment can modify the text of the task assignment and it has some additional information which is specific to the group (e.g., a deadline, the number of points gained for a correct solution, additional hints for the students in the assignment). The text of the assignment can be edited and supervisors can translate the assignment into another language.

Solution

A solution is a set of files which a user submits to a given assignment.

Submission

A submission corresponds to a solution being evaluated by the Backend. A single solution can be submitted repeatedly (e.g., when the Backend encounters an error or when the supervisor changes the assignment).

Evaluation

An evaluation is the processed report received from the Backend after a submission is processed. Evaluation contains points given to the user based on the quality of his solution measured by the Backend and the settings of the assignment. Supervisors can review the evaluation and add bonus points (both positive and negative) if the student deserves some.

Runtime environment

A runtime environment defines the used programming language or tools which are needed to process and evaluate a solution. Examples of a runtime environment can be:

• Linux + GCC
• Linux + Mono
• Windows + .NET 4
• Bison + Yacc

Limits

A correct solution of an assignment has to pass all specified tests (mostly checks that it yields the correct output for various inputs) and typically must also be effective in some sense. The Backend measures the time and memory consumption of the solution while running. This consumption of resources can be limited and the solution will receive fewer points if it exceeds the given limits in some test cases defined by the exercise.

User management

@todo: roles and their rights, adding/removing different users, how the role of a specific user changes

Instances and hierarchy of groups

@todo: What is an instance, how to create one, what are the licenses and how do they work. Why can the groups form hierarchies and what are the benefits – what it means to be an admin of a group, hierarchy of roles in the group hierarchy.

Exercises database

@todo: How the exercises are stored, accessed, who can edit what

Creating a new exercise

@todo Localized assignments, default settings

Runtime environments and hardware groups

@todo read this later and see if it still makes sense

ReCodEx is designed to utilize a rather diverse set of workers -- there can be differences in many aspects, such as the actual hardware running the worker (which impacts the results of measuring) or installed compilers, interpreters and other tools needed for evaluation. To address these two examples in particular, we assign runtime environments and hardware groups to exercises.

The purpose of runtime environments is to specify which tools (and often also operating system) are required to evaluate a solution of the exercise -- for example, a C# programming exercise can be evaluated on a Linux worker running Mono or a Windows worker with the .NET runtime. Such exercise would be assigned two runtime environments, Linux+Mono and Windows+.NET (the environment names are arbitrary strings configured by the administrator).

A hardware group is a set of workers that run on similar hardware (e.g. a particular quad-core processor model and a SSD hard drive). Workers are assigned to these groups by the administrator. If this is done correctly, performance measurements of a submission should yield the same results. Thanks to this fact, we can use the same resource limits on every worker in a hardware group. However, limits can differ between runtime environments -- formally speaking, limits are a function of three arguments: an assignment, a hardware group and a runtime environment.

Reference solutions

@todo: how to add one, how to evaluate it

The task of determining appropriate resource limits for exercises is difficult to do correctly. To aid exercise authors and group supervisors, ReCodEx supports assigning reference solutions to exercises. Those are example programs that should cover the main approaches to the implementation. For example, searching for an integer in an ordered array can be done with a linear search, or better, using a binary search.

Reference solutions can be evaluated on demand, using a selected hardware group. The evaluation results are stored and can be used later to determine limits. In our example problem, we could configure the limits so that the linear search-based program doesn't finish in time on larger inputs, but a binary search does.

Note that separate reference solutions should be supplied for all supported runtime environments.

Exercise assignments

@todo: Creating instances of an exercise for a specific group of users, capabilities of settings. Editing limits according to the reference solution.

Evaluation process

@todo: How the evaluation process works on the Frontend side.

Uploading files and file storage

@todo: One by one upload endpoint. Explain different types of the Uploaded files.

Automatic detection of the runtime environment

@todo: Users must submit correctly named files – assuming the RTE from the extensions.

REST API implementation

@todo: What is the REST API, what are the basic principles – GET, POST, Headers, JSON.

Authentication and authorization scopes

@todo: How authentication works – signed JWT, headers, expiration, refreshing. Token scopes usage.

HTTP requests handling

@todo: Router and routes with specific HTTP methods, preflight, required headers

HTTP responses format

@todo: Describe the JSON structure convention of success and error responses

Used technologies

@todo: PHP7 – how it is used for typehints, Nette framework – how it is used for routing, Presenters actions endpoints, exceptions and ErrorPresenter, Doctrine 2 – database abstraction, entities and repositories + conventions, Communication over ZMQ – describe the problem with the extension and how we reported it and how to treat it in the future when the bug is solved. Relational database – we use MariaDB, Doctine enables us to switch the engine to a different engine if needed

Data model

@todo: Describe the code-first approach using the Doctrine entities, how the entities map onto the database schema (refer to the attached schemas of entities and relational database models), describe the logical grouping of entities and how they are related:

• user + settings + logins + ACL
• instance + licenses + groups + group membership
• exercise + assignments + localized assignments + runtime environments + hardware groups
• submission + solution + reference solution + solution evaluation
• comment threads + comments

API endpoints

@todo: Tell the user about the generated API reference and how the Swagger UI can be used to access the API directly.

Web Application

@todo: What is the purpose of the web application and how it interacts with the REST API.

Used technologies

@todo: Briefly introduce the used technologies like React, Redux and the build process. For further details refer to the GitHub wiki

How to use the application

@todo: Describe the user documentation and the FAQ page.

Backend-Frontend communication protocol

@todo: describe the exact methods and respective commands for the communication

Initiation of a job evaluation

@todo: How does the Frontend initiate the evaluation and how the Backend can accept it or decline it

Job processing progress monitoring

When evaluating a job the worker sends progress messages on predefined points of evaluation chain. The sending place can be on very beginning of the job, when submit archive is downloaded or at the end of each simple task with its state (completed, failed, skipped). These messages are sent to broker through existing ZeroMQ connection. Detailed format of messages can be found on communication page.

Broker only resends received progress messages to the monitor component via ZeroMQ socket. The output message format is the same as the input format.

Monitor parses received messages to JSON format, which is easy to work with in JavaScript inside web application. All messages are cached (one queue per job) and can be obtained multiple times through WebSocket communication channel. The cache is cleared 5 minutes after receiving last message.

Publishing of the results

After job finish the worker packs results directory into single archive and uploads it to the fileserver through HTTP protocol. The target URL is obtained from API in headers on job initiation. Then "job done" notification request is performed to API via broker. Special submissions (reference or asynchronous submissions) are loaded immediately, other types are loaded on-demand on first results request.

Loading results means fetching archive from fileserver, parsing the main YAML file generated by worker and saving data to the database. Also, points are assigned by score calculator.

User documentation

Web Application

@todo: Describe different scenarios of the usage of the Web App

Terminology

@todo: Describe the terminology: Instance, User, Group, Student, Supervisor, Admin

Web application requirements

@todo: Describe the requirements of running the web application (modern web browser, enabled CSS, JavaScript, Cookies & Local storage)

Scenario #1: Becoming a user of ReCodEx

How to create a user account?

You can create an account if you click on the “Create account” menu item in the left sidebar. You can choose between two types of registration methods – by creating a local account with a specific password, or pairing your new account with an existing CAS UK account.

If you decide a new “local” account using the “Create ReCodEx account” form, you will have to provide your details and choose a password for your account. You will later sign in using your email address as your username and the password you select.

If you decide to use the CAS UK, then we will verify your credentials and access your name and email stored in the system and create your account based on this information. You can change your personal information or email later on the “Settings” page.

When crating your account both ways, you must select an instance your account will belong to by default. The instance you will select will be most likely your university or other organization you are a member of.

How to get into ReCodEx?

To log in, go to the homepage of ReCodEx and in the left sidebar choose the menu item “Sign in”. Then you must enter your credentials into one of the two forms – if you selected a password during registration, then you should sign with your email and password in the first form called “Sign into ReCodEx”. If you registered using the Charles University Authentication Service (CAS), you should put your student’s number and your CAS password into the second form called “Sign into ReCodEx using CAS UK”.

How do I sign out of ReCodEx?

If you don’t use ReCodEx for a whole day, you will be logged out automatically. However, we recommend you sign out of the application after you finished your interaction with it. The logout button is placed in the top section of the left sidebar right under your name. You will have to expand the sidebar with a button next to the “ReCodEx” title (shown in the picture below).

@todo: Simon's image

What to do when you cannot remember your password?

If you can’t remember your password and you don’t use CAS UK authentication, then you can reset your password. You will find a link saying “You cannot remember what your password was? Reset your password.” under the sign in form. After you click on this link, you will be asked to submit your email address. An email with a link containing a special token will be sent to the address you fill in. We make sure that the person who requested password resetting is really you. When you click on the link (or you copy & paste it into your web browser) you will be able to select a new password for your account. The token is valid only for a couple of minutes, so do not forget to reset the password as soon as possible, or you will have to request a new link with a valid token.

If you sign in through CAS UK, then please follow the instructions provided by the administrators of the service described on their website.

How to configure your account?

There are several options you have to edit your user account.

• changing your personal information (i.e., name)
• changing your credentials (email and password)
• updating your preferences (e.g., source code viewer/editor settings, default language)

You can access the settings page through the “Settings” button right under your name in the left sidebar.

Scenario #2: User is a student

@todo: describe what it means to be a “student” and what are the student’s rights

How to join a group for my class?

@todo: How to join a specific group

Which assignments do I have to solve?

@todo: Where the student can find the list of the assignment he is expected to solve, what is the first and second deadline.

Where can I see details of my classes’ group?

@todo: Where can the user see groups description and details, what information is available.

How to submit a solution of an assignment?

@todo: How does a student submit his solution through the web app

Where are the results of my solutions?

@todo: When the results are ready and what the results mean and what to do about them, when the user is convinced, that his solution is correct although the results say different

How can I discuss my solution with my teacher/group’s supervisor directly through the web application?

@todo: Describe the comments thread behavior (public/private comments), who else can see the comments, how notifications work (not implemented yet!).

Scenario #3: User is supervisor of a group

@todo: describe what it means to be a “supervisor” of a group and what are the supervisors rights

How do I become a supervisor of a group?

@todo: How does a user become a supervisor of a group?

How to add or remove a student to my group?

@todo: How to add a specific student to a given group

How do I add another supervisor to my group?

@todo: who can add another supervisor, what would be the rights of the second supervisor

How do I create a subgroup of my group?

@todo: What it means to create a subgroup and how to do it.

How do I assign an exercise to my students?

@todo: Describe how to access the database of the exercises and what are the possibilities of assignment setup – availability, deadlines, points, score configuration, limits

How do I configure the limits of an assignment and how to choose appropriate limits?

@todo: Describe the form and explain the concept of reference solutions. How to evaluate the reference solutions for the exercise right now (to get the up-to-date information).

How can I assign some exercises only to some students of the group?

@todo: Describe how to achieve this using subgroups

How can I see my students’ solutions?

@todo Describe where all the students’ solutions for a given assignment can be found, where to look for all solutions of a given student, how to see results of a specific student’s solution’s evaluation result.

Can I assign points to my students’ solutions manually instead of depending on automatic scoring?

@todo If and how to change the score of a solution – assignment settings, setting points, bonus points, accepting a solution (not implemented yet!). Describe how the student and supervisor will still be able to see the percentage received from the automatic scoring, but the awarded points will be overridden.

How can I discuss student’s solution with him/her directly through the web application?

@todo: Describe the comments thread behavior (public/private comments), who else can see the comments -- same as from the student perspective

Writing job configuration

To run and evaluate an exercise the backend needs to know the steps how to do that. This is different for each environment (operation system, programming language, etc.), so each of the environments needs to have separate configuration.

Backend works with a powerful, but quite low level description of simple connected tasks written in YAML syntax. More about the syntax and general task overview can be found on separate page. One of the planned features was user friendly configuration editor, but due to tight deadline and team composition it did not make it to the first release. However, writing configuration in the basic format will be always available and allows users to use the full expressive power of the system.

This section walks through creation of job configuration for hello world exercise. The goal is to compile file source.c and check if it prints Hello World! to the standard output. This is the only test case, let's call it A.

The problem can be split into several tasks:

• compile source.c into helloworld with /usr/bin/gcc
• run helloworld and save standard output into out.txt
• fetch predefined output (suppose it is already uploaded to fileserver) with hash a0b65939670bc2c010f4d5d6a0b3e4e4590fb92b to reference.txt
• compare out.txt and reference.txt by /usr/bin/diff

The absolute path of tools can be obtained from system administrator. However, /usr/bin/gcc is location, where the GCC binary is available almost everywhere, so location of some tools can be (professionally) guessed.

First, write header of the job to the configuration file.

submission:
    job-id: hello-word-job
    hw-groups:
        - group1

Basically it means, that the job hello-world-job needs to be run on workers that belong to the group_1 hardware group . Reference files are downloaded from the default location configured in API (such as http://localhost:9999/exercises) if not stated explicitly otherwise. Job execution log will not be saved to result archive.

Next the tasks have to be constructed under tasks section. In this demo job, every task depends only on previous one. The first task has input file source.c (if submitted by user) already available in working directory, so just call the GCC. Compilation is run in sandbox as any other external program and should have relaxed time and memory limits. In this scenario, worker defaults are used. If compilation fails, the whole job is immediately terminated (because the fatal-failure bit is set). Because bound-directories option in sandbox limits section is mostly shared between all tasks, it can be set in worker configuration instead of job configuration (suppose this for following tasks). For configuration of workers please contact your administrator.

- task-id: "compilation"
  type: "initiation"
  fatal-failure: true
  cmd:
      bin: "/usr/bin/gcc"
      args:
          - "source.c"
          - "-o"
          - "helloworld"
  sandbox:
      name: "isolate"
      limits:
          - hw-group-id: group1
            chdir: ${EVAL_DIR}
            bound-directories:
                - src: ${SOURCE_DIR}
                  dst: ${EVAL_DIR}
                  mode: RW

The compiled program is executed with time and memory limit set and the standard output is redirected to a file. This task depends on compilation task, because the program cannot be executed without being compiled first. It is important to mark this task with execution type, so exceeded limits will be reported in frontend.

Time and memory limits set directly for a task have higher priority than worker defaults. One important constraint is, that these limits cannot exceed limits set by workers. Worker defaults are present as a safety measure so that a malformed job configuration cannot block the worker forever. Worker default limits should be reasonably high, like a gigabyte of memory and several hours of execution time. For exact numbers please contact your administrator.

It is important to know that if the output of a program (both standard and error) is redirected to a file, the sandbox disk quotas apply to that file, as well as the files created directly by the program. In case the outputs are ignored, they are redirected to /dev/null, which means there is no limit on the output length (as long as the printing fits in the time limit).

- task-id: "execution_1"
  test-id: "A"
  type: "execution"
  dependencies:
      - compilation
  cmd:
      bin: "helloworld"
  sandbox:
      name: "isolate"
      stdout: ${EVAL_DIR}/out.txt
      limits:
          - hw-group-id: group1
            chdir: ${EVAL_DIR}
            time: 0.5
            memory: 8192

Fetch sample solution from file server. Base URL of file server is in the header of the job configuration, so only the name of required file (its sha1sum in our case) is necessary.

- task-id: "fetch_solution_1"
  test-id: "A"
  dependencies:
      - execution
  cmd:
      bin: "fetch"
      args:
          - "a0b65939670bc2c010f4d5d6a0b3e4e4590fb92b"
          - "${SOURCE_DIR}/reference.txt"

Comparison of results is quite straightforward. It is important to set the task type to evaluation, so that the return code is set to 0 if the program is correct and 1 otherwise. We do not set our own limits, so the default limits are used.

- task-id: "judge_1"
  test-id: "A"
  type: "evaluation"
  dependencies:
      - fetch_solution_1
  cmd:
      bin: "/usr/bin/diff"
      args:
          - "out.txt"
          - "reference.txt"
  sandbox:
      name: "isolate"
      limits:
          - hw-group-id: group1
            chdir: ${EVAL_DIR}