SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
In: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Patched functions often remain similar to vulnerable ones, and a new multi-model similarity scoring system identifies residual issues like null pointer dereferences in 61% of high-risk cases from the PrimeVul dataset.
Empirical analysis of 338 PRs with self-admitted ChatGPT usage shows low full integration (median 25%), selective adaptation patterns, and broader influence on developer reasoning during reviews.
citing papers explorer
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ML Code Smells: From Specification to Detection
SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
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Residual Risk Analysis in Benign Code: How Far Are We? A Multi-Model Semantic and Structural Similarity Approach
Patched functions often remain similar to vulnerable ones, and a new multi-model similarity scoring system identifies residual issues like null pointer dereferences in 61% of high-risk cases from the PrimeVul dataset.
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PatchTrack: A Comprehensive Analysis of ChatGPT's Influence on Pull Request Outcomes
Empirical analysis of 338 PRs with self-admitted ChatGPT usage shows low full integration (median 25%), selective adaptation patterns, and broader influence on developer reasoning during reviews.