Author Identification of Software Source Code with Program Dependence Graphs

Chen, Rong; Hong, Lina; Lu, Chin‐Song; Deng, Wen

doi:10.1109/compsacw.2010.56

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…However, they use semantic information which is not denoted through the grammar of programming languages. So, they typically encode information using graphs, such as program dependence graphs [52,210], control-low graphs [45], and call graphs [187], rather than tokens and trees. Other semantic approaches involve analyzing the similarity of relationships between terms through latent semantic analysis [23,58,237].…”

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

show abstract

“…with graph edit distance or sub-graph embedding) to identify indications of plagiarism (e.g. [26,27,28]). While other methods include applying latent semantic analysis to identify similarly referenced terms [29] or identifying similar call graph structures [30].…”

Section: Background a Measuring Program Similaritymentioning

confidence: 99%

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

2021

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Source code plagiarism is a long-standing issue in tertiary computer science education. Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism. However, existing detection tools are not robust to pervasive plagiarism-hiding transformations and can be inaccurate in the detection of plagiarised source code. This article presents BPlag, a behavioural approach to source code plagiarism detection. BPlag is designed to be both robust to pervasive plagiarism-hiding transformations and accurate in the detection of plagiarised source code. Greater robustness and accuracy is afforded by analysing the behaviour of a program, as behaviour is perceived to be the least susceptible aspect of a program impacted upon by plagiarism-hiding transformations. BPlag applies symbolic execution to analyse execution behaviour and represents a program in a novel graph-based format. Plagiarism is then detected by comparing these graphs and evaluating similarity scores. BPlag is evaluated for robustness, accuracy and efficiency against five commonly used source code plagiarism detection tools. It is then shown that BPlag is more robust to plagiarism-hiding transformations and more accurate in the detection of plagiarised source code, but is less efficient than the compared tools.INDEX TERMS source code plagiarism detection, behavioural similarity, source code similarity.

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“…Like tree-based approaches, graph-based approaches also suffer from high computational complexity in analysing similarity (Baxter et al 1998). Liu et al (2006), and Chen et al (2010) implement such methods by evaluating the similarity of PDGs. These methods are claimed to be immune to plagiarismhiding modifications such as statement reordering, and mapping statements to semantic equivalents.…”

Section: Source Code Plagiarism Detection Toolsmentioning

confidence: 99%

Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism-hiding modifications

Cheers¹,

Yuan²,

Smith³

2021

Preprint

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Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools (specifically those that compare programs as program dependence graphs) show potentially greater robustness to pervasive plagiarism-hiding modifications.

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Author Identification of Software Source Code with Program Dependence Graphs

Cited by 12 publications

References 7 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

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism-hiding modifications

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