Comparing Jailed Sandboxes vs Containers Within an Autograding System

Peveler, Matthew; Maicus, Evan; Cutler, Barbara

doi:10.1145/3287324.3287507

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…Although most tools still rely on user permissions [2,60], jails [40,164,203], and JVM [115,172,242] security mechanisms, several new tools [156,180,226] are adopting containerization, particularly through Docker containers, to perform the dynamic analysis of students' programs. Docker reduces the entry barrier to using containers, which enables their fast-growing popularization lately, as containers also ofer a similar level of isolation to that of a VM while keeping a much lower overhead, slightly above other solutions depending on the image [178]. In addition to that, being able to run the code in a clean environment with only the speciic version of the language compiler and libraries needed allows great lexibility and reduces compatibility issues.…”

Section: Code Execution (Rq3)mentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

113

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Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

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Section: Code Execution (Rq3)mentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

113

View full text Add to dashboard Cite

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“…Dynamic source code analysis is carried out afterward in order to verify the correctness of the submitted code on both open and hidden tests. The code is executed in a jailed Docker-based sandbox [31] with the gVisor secure runtime [32]. If an error has occurred while performing the tests, the DTA system only demonstrates the input data of only one erroneous test case.…”

Section: Digital Teaching Assistant Corementioning

confidence: 99%

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

Demidova

Andrianova

Sovetov

et al. 2023

Data

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This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students.

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“…To cope with this diversity, Dodona uses a generic infrastructure for automated assessment, consisting of three loosely coupled components: a container, a judge, and an assignment-specific assessment configuration. A Docker container [21] is used for proper virtualization. It defines the runtime environment in which all data and executables are provided.…”

Section: Automated Assessmentmentioning

confidence: 99%

Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning

Petegem

Dawyndt

Mesuere

2023

Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education v. 2

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Dodona () is an intelligent tutoring system for computer programming. It provides real-time data and feedback to help students learn better and teachers teach better.Dodona is free to use and has more than 61 thousand registered users across many educational and research institutes, including 20 thousand new users in the last year. The source code of Dodona is available on GitHub under the permissive MIT open-source license.This paper presents Dodona and its design and look-and-feel. We highlight some of the features built into Dodona that make it possible to shorten feedback loops, and discuss an example of how these features can be used in practice. We also highlight some of the research opportunities that Dodona has opened up and present some future developments. Code metadataCurrent code version 2023.08 Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-23-00106 Code Ocean compute capsule n/a Legal Code License MIT Code versioning system used git Software code languages, tools, and services used Ruby 3.1 (Ruby-on-Rails 7.0), JavaScript, TypeScript Compilation requirements, operating environments & dependencies Ruby 3.1, yarn 1.22, Unix-like (e.g. Ubuntu 22.04), Memcached 1.6.14, MySQL 8 If available Link to developer documentation/manual https://docs.dodona.be Support email for questions

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Comparing Jailed Sandboxes vs Containers Within an Autograding System

Cited by 16 publications

References 12 publications

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning

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