Plagiarism in programming assignments

Joy, Mike; Lück, Michael

doi:10.1109/13.762946

Cited by 232 publications

(170 citation statements)

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“…Researchers (Joy and Luck, 1999;Daly and Horgan, 2005;Spinellis et al, 2007) have identified plagiarism issues in many perspectives. Unacknowledged copying of documents or programs is considered as an act of plagiarism.…”

Section: Related Workmentioning

confidence: 99%

“…With rampant increment in plagiarism phenomena (Joy and Luck, 1999;Sheard and Dick, 2003) necessary efforts should be taken to provide a conducive environment that encourages students to develop their programming skills (Cogan and Gurwitz, 2009;Sani et al, (2009). Since it is so easy to copy and edit a computer program, students would find it is tempting to get involve in plagiarism activity.…”

Section: Introductionmentioning

confidence: 99%

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Activity Surveillance and Hawthorne Effect to Prevent Programming Plagiarism

Judi

Salleh

Hussin

et al. 2010

Journal of Computer Science

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Problem statement: Course instructors are facing serious problems in dealing with students who plagiarize programs especially when the number of students in the course is high. Among the proposed approach to handle this problem is by using automatic detection of plagiarism in programming projects. Preventive action is required rather than curing the problem so that programming students get the right message from the beginning. Approach: To address this problem, a surveillance system was proposed to record every programming activity. It is developed in an integrated development environment so that programming activity profile in Java format is created when students are developing their Java program. A non-intrusive and non-experimental setting approach was applied in which hidden data collection is conducted to observe students' behavior in natural programming setting. Experimental study effect i.e., Hawthorne effect and effect of expectation on subject behavior was exploited as prevention on plagiarism. Surveillance system produces two file types: Activity log to keep programming activity log information and Backup file to save the program writing record. Results: The proposed programming activity surveillance system, DwiCoder presented a programming activity report at the end of each programming session. Students can assess their own progress in developing a program in these three activities: Compilation, execution and modification. The report was presented in a simple and meaningful way to encourage student spend their own time in programming activity. Conclusion: By using DwiCoder, student's programming activity is continuously monitored and their behavior is under control. This system provides an effective prevention method in tackling plagiarism.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity Surveillance and Hawthorne Effect to Prevent Programming Plagiarism

Judi

Salleh

Hussin

et al. 2010

Journal of Computer Science

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show abstract

“…Different similarity measurements such as suffix trees, string alignment, Jaccard similarity, etc., can be applied to sequences or sets of tokens. Tools that rely on tokens include Sherlock (Joy and Luck 1999), BOSS (Joy et al 2005), Sim (Gitchell and Tran 1999), YAP3 (Wise 1996), JPlag (Prechelt et al 2002), CCFinder (Kamiya et al 2002), CP-Miner (Li et al 2006), MOSS (Schleimer et al 2003), Burrows et al (2007), and the Source Code Similarity Detector System (SCSDS) (Duric and Gasevic 2013). The token-based representation is widely used in source code similarity measurement and very efficient on a scale of millions SLOC.…”

Section: Code Similarity Measurementmentioning

confidence: 99%

A comparison of code similarity analysers

2017

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Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

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“…However, we can examine the reports that are produced by different plagiarism detection software when used on the same dataset. The systems used for the analysis include MOSS [6], JPlag [5] and Sherlock [3]. Every system printed a report about the same real collection, consisting of 220 undergraduate student's Java programs.…”

Section: Similarity(q F I ) = M Atchedt Okens(fmentioning

confidence: 99%

“…Modern plagiarism detectors, such as Sherlock [3], JPlag [5] and MOSS [6] use a tokenization technique to improve detection. These detectors work by pre-processing code to remove white-space and comments before converting the file into a tokenized string.…”

mentioning

confidence: 99%