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Researchers evaluate code metrics for detecting software plagiarism

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates whether standard code evaluation metrics can effectively detect plagiarism in source code. The study compared five metrics—CodeBLEU, CrystalBLEU, RUBY, TSED, and CodeBERTScore—against specialized plagiarism detection tools like JPlag and Dolos. Results indicate that these metrics can be comparable to dedicated tools, especially CrystalBLEU when combined with preprocessing, and perform best on less modified code. AI

IMPACT Evaluates the effectiveness of code evaluation metrics for plagiarism detection, potentially impacting academic integrity tools.

RANK_REASON This is a research paper evaluating existing metrics for a specific task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers evaluate code metrics for detecting software plagiarism

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mike Joy ·

    Can Code Evaluation Metrics Detect Code Plagiarism?

    Source Code Plagiarism Detection (SCPD) plays an important role in maintaining fairness and academic integrity in software engineering education. Code Evaluation Metrics (CEMs) are developed for assessing code generation tasks. However, it remains unclear whether such metrics can…

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