The assessment of programming courses is usually carried out by means of programming assignments. Since it is simple to copy and edit computer programs, however, there will always be a temptation among some students following such courses to copy and modify the work of others. As the number of students in these courses is often high, it can be very difficult to detect this plagiarism. The authors have developed a package which will allow programming assignments to be submitted online, and which includes software to assist in detecting possible instances of plagiarism. In this paper, they discuss the concerns that motivated this work, describe the developed software, tailoring the software to different requirements and finally consider its implications for large group teaching.

Computer programming lends itself to automated assessment. With appropriate software tools, program correctness can be measured, along with an indication of quality according to a set of metrics. Furthermore, the regularity of program code allows plagiarism detection to be an integral part of the tools that support assessment. In this paper, we describe a submission and assessment system, called BOSS, that supports coursework assessment through collecting submissions, performing automatic tests for correctness and quality, checking for plagiarism, and providing an interface for marking and delivering feedback. We describe how automated assessment is incorporated into BOSS such that it supports, rather than constrains, assessment. The pedagogic and administrative issues that are affected by the assessment process are also discussed.

Plagiarism is a growing problem in academia. Academics often use plagiarism detection tools to detect similar source-code files. Once similar files are detected, the academic proceeds with the investigation process which involves identifying the similar source-code fragments within them that could be used as evidence for proving plagiarism. This paper describes PlaGate, a novel tool that can be integrated with existing plagiarism detection tools to improve plagiarism detection performance. The tool also implements a new approach for investigating the similarity between source-code files with a view to gathering evidence for proving plagiarism. Graphical evidence is presented that allows for the investigation of source-code fragments with regards to their contribution toward evidence for proving plagiarism. The graphical evidence indicates the relative importance of the given source-code fragments across files in a corpus. This is done by using the Latent Semantic Analysis information retrieval technique to detect how important they are within the specific files under investigation in relation to other files in the corpus.

A survey using a scenario-based questionnaire format has provided insight into the perceptions of U.K academics who teach programming on computing courses. This survey across various higher education (HE) institutions investigates what academics feel constitutes source-code plagiarism in an undergraduate context. Academics' responses on issues surrounding source-code reuse and acknowledgement are discussed. A general consensus exists among academics that a ldquozero tolerancerdquo plagiarism policy is appropriate; however, some issues concerning source-code reuse and acknowledgement raised controversial responses. This paper discusses the most important findings from the survey and proposes a definition of what can constitute source-code plagiarism from the perspective of U.K. academics who teach programming on computing courses.

Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”