Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Cheers, Hayden; Lin, Yuqing; Smith, Shamus P.

doi:10.1109/access.2021.3069367

Cited by 36 publications

(28 citation statements)

References 50 publications

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“…Several distinct techniques exist for comparing programs' behavior, difering in which dynamic aspect they observe. For instance, there are techniques analysing data at runtime [48,120], interactions with the execution environment [14,48], functionality [145], and logic [152,256].…”

Section: Functionality (Source Code)mentioning

confidence: 99%

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

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

106

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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: Functionality (Source Code)mentioning

confidence: 99%

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

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

106

View full text Add to dashboard Cite

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“…Alternative techniques for source code plagiarism detection that have been explored include tree-based algorithms (Li & Zhong, 2010;Zhao et al, 2015), graph-based algorithms (Chae et al, 2013;Liu et al, 2006), Latent Semantic Analysis information retrieval (Cosma & Joy, 2012), fuzzy-based matching (Acampora & Cosma, 2015), program logic analysis (Cheers et al, 2019), and program behavioural analysis (Cheers et al, 2021). However, while some yield promising initial results, neither of these approaches have gone beyond the proof-of-concept stage and resulted in tools that are applicable in practice to confirm the authors' own conclusions.…”

Section: Educational Source Code Plagiarism Detectionmentioning

confidence: 99%

Dolos: Language‐agnostic plagiarism detection in source code

Maertens

Petegem

Strijbol

et al. 2022

Computer Assisted Learning

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Background Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state‐of‐the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods Dolos is compared with state‐of‐the‐art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID‐19 pandemic. Results and Conclusions Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open‐source license at https://dolos.ugent.be. Implications Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment.

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“…Therefore, a fingerprinting method was proposed instead of the parse tree kernel. Authors in [21] introduced a behavioral approach called BPlag to detect source code plagiarism. The behavior was extracted using symbolic execution.…”

Section: A Software Piracy Detectionmentioning

confidence: 99%

A Deep Learning Approach for Malware and Software Piracy Threat Detection

Aldriwish

2021

Eng. Technol. Appl. Sci. Res.

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Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

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Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Cited by 36 publications

References 50 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

Dolos: Language‐agnostic plagiarism detection in source code

A Deep Learning Approach for Malware and Software Piracy Threat Detection

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