Code Comments: A Way of Identifying Similarities in the Source Code

Rares Folea () and Emil Slusanschi
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Rares Folea: Department of Computer Science and Engineering, Faculty for Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania
Emil Slusanschi: Department of Computer Science and Engineering, Faculty for Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania

Mathematics, 2024, vol. 12, issue 7, 1-22

Abstract: This study investigates whether analyzing the code comments available in the source code can effectively reveal functional similarities within software. The authors explore how both machine-readable comments (such as linter instructions) and human-readable comments (in natural language) can contribute towards measuring the code similarity. For the former, the work is relying on computing the cosine similarity over the one-hot encoded representation of the machine-readable comments, while for the latter, the focus is on detecting similarities in English comments, using threshold-based computations against the similarity measurements obtained using models based on Levenshtein distances (for form-based matches), Word2Vec (for contextual word representations), as well as deep learning models, such as Sentence Transformers or Universal Sentence Encoder (for semantic similarity). For evaluation, this research has analyzed the similarities between different source code versions of the open-source code editor, VSCode, based on existing ESlint-specific directives, as well as applying natural language processing techniques on incremental releases of Kubernetes, an open-source system for automating containerized application management. The experiments outlines the potential for detecting code similarities solely based on comments, and observations indicate that models like Universal Sentence Encoder are providing a favorable balance between recall and precision. This research is integrated into Project Martial, an open-source project for automatic assistance in detecting plagiarism in software.

Keywords: code similarity; linter analysis; comments analysis; software plagiarism; plagiarism detection; universal sentence encoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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