Automatic evaluation of correctness and originality of source codes

Pohuba, Daniel; Dulík, Tomáš; Janku, Peter

doi:10.1109/ewme.2014.6877393

Cited by 5 publications

(5 citation statements)

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“…Significant component of such system is the source code originality control. In the first version [12] of our system, YAP [28] tool was used; unfortunately it is too outdate to meet today's demands. For a plagiarism module more research is needed, however based on our preliminary analysis we are planning to implement this module as a multilayered detection mechanism [29], to improve pattern matching results.…”

Section: Resultsmentioning

confidence: 99%

“…The first version [12] of our system was successfully run during summer term in 2013/14 academic year within course "Algorithms and data structures" at Faculty of Applied Informatics, Thomas Bata University in Zlin. Feedback from the course was subsequently used to improve list of requirements and functionalities for next version of the system.…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Docker as Platform for Assignments Evaluation

Špaček

Sohlich

Dulík

2015

Procedia Engineering

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Programming courses are significant part of IT experts' education process. To being able to provide adequate teaching quality in such courses, lecturers should be exempted from routine tasks like source code compilation, testing and grading. Current computers are equipped with enough computational power to automate these routine tasks. This paper discusses the analysis and realization of such a system for user submitted automatic source code evaluation. The main system requirement was the safe runtime environment (sandbox) for executing potentially dangerous programs. Container based platform Docker was selected after research of ready to use sandbox technologies. This platform simplifies access to isolation mechanism which are implemented in the current Linux kernel and provides API for system integration. The implemented system around Docker platform is named APAC (Automatic Programming Assignment Checker). In the paper APAC's architecture and implementation are described and discussed.

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Section: Resultsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Docker as Platform for Assignments Evaluation

Špaček

Sohlich

Dulík

2015

Procedia Engineering

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“…After passing from the first to the seventh stage, the SMS found 33 studies for results analysis in the last stage to answer the three research questions [Chan et al 2013, Choi et al 2013, Kim et al 2013, Tian et al 2013, Zhang and Liu 2013, Ajmal et al 2014, Baby et al 2014, Kikuchi et al 2014, Lazar and Banias 2014, Pohuba et al 2014, Zhang et al 2014, Acompora and Cosma 2015, Dutta 2015, Jhi et al 2015, Oprişa and Ignat 2015, Sharma et al 2015, Soh et al 2015, Tian et al 2015, Ming et al 2016, Strilețchi et al 2016, Agrawal and Sharma 2017, Jain et al 2017, Kargén and Shahmehri 2017, Luo et al 2017, Mirza et al 2017, Mišić et al 2017, Schneider et al 2017, Sudhamani and Rangarajan 2017, Karnalim 2018, Roopam and Singh 2018.…”

Section: Resultsmentioning

confidence: 99%

Detection of Programming Plagiarism in Computing Education: A Systematic Mapping Study

Gomes

Matos

2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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The programming plagiarism is increasingly a problem in computing education, and the proposed solutions for this growing concern rely on automatic detectors. The usage of the automatic tools for this purpose can provide benefits in education for professors and instructors of programming assignments, besides, to avoid the lack of essential skills from the students since they compromise their programming logic by plagiarizing. This paper performs a systematic mapping study aligned with a snowballing technique to analyzes the existing solutions for this domain. As contributions, tendencies, as well as information analysis, are provided to guide new proposals of solutions.

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“…Viji Gopal, Varghese Paul, M Sudheep Elayidom, Sasi Gopalan approach to plagiarism detection in academic documents that integrate detection methods using citations, semantic argument structure, and semantic word similarity with character-based methods to achieve a higher detection performance for disguised plagiarism forms. Automatic correctness checking is introduced in the paper [3], which is searching for plagiarisms in assignments submitted and checking the correct implementation of algorithms. Another work is a code clone-detection algorithm for finding Type-3 clone i.e.…”

Section: Related Workmentioning

confidence: 99%

Similarity Detection in Large Volume Data using Machine Learning Techniques

Gopal*¹,

Paul²,

Elayidom³

et al. 2019

IJRTE

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When unauthorized copying or stealing of intellectual properties of others happen, it is called plagiarism. Two main approaches are used to counter this problem – external plagiarism detection and intrinsic plagiarism detection. External algorithms compare a suspicious file with numerous sources whereas intrinsic algorithms are allowed to solely inspect the suspicious file in order to predict plagiarism. In this work, the area chosen for detecting plagiarism is with programs or source code files. Copying the entire source code or logic used in a particular program without permissions or copyright is the stealing that happens in the case of source codes. There exist many ways to detect plagiarism in source code files. To perform plagiarism checking for a large dataset, the computational cost is very high and moreover it’s a time consuming job. To achieve a computationally efficient similarity detection in source code files, the Hadoop framework is used where parallel computation is possible for large datasets. But the raw data available to us is not in a suitable form for the existing plagiarism checking tools to work with, as their size is too high and they possess features of big data. Thus a qualifying model is required for the dataset, to be fed into Hadoop so that it could efficiently process them to check for plagiarism in source codes. To generate such a model, machine learning is used which incorporates big data with machine learning

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Automatic evaluation of correctness and originality of source codes

Cited by 5 publications

References 0 publications

Docker as Platform for Assignments Evaluation

Docker as Platform for Assignments Evaluation

Detection of Programming Plagiarism in Computing Education: A Systematic Mapping Study

Similarity Detection in Large Volume Data using Machine Learning Techniques

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