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M. Nagappan

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Open access Aug 2026

SNIPTEST: Fuzzing Multi-Level Code Slices for Validating Vulnerabilities

SNIPTEST is an execution-based warning triage framework that generates and fuzzes compiled code slices centered around static-analysis warnings that employs a layer-by-layer slicing strategy, incrementally expanding context around the target location to validate potential vulnerabilities with increasing precision.

Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel et al. · 0 citations
Review Aug 2026

COMMITGUARD: Differential Slice Fuzzing for Commit-Induced Bug Detection

The key insight behind COMMITGUARD is that the pre-commit version of a modified function can serve as a behavioral baseline for interpreting bugs found after the commit, and a commit-aware differential slice-based fuzzing approach for verifying code changes is introduced.

Aniruddhan Murali, N. Mathews, Mahmoud Alfadel et al. · 0 citations
Jul 2026

What Makes a Good Bug Report for an AI Agent?

It is found that a good bug report for an agent overlaps with, but is not identical to, a good report for a human: agents benefit most from concrete, executable, and well-localized information, whereas some qualities long emphasized for human readers, such as natural language steps to reproduce and readable description...

Lara Khatib, N. Mathews, M. Nagappan et al. · 2 citations · ⚡1
Preprint Jul 2026

Functional and Secure Code Generation with Task Vectors

A novel method is developed, SecVecCoder, leveraging task vectors to produce trustworthy code that is simultaneously functional and secure without the need for post-generation adjustment, and achieves a decoding latency within 0.6% of the base model's, on average.

F. Wang, Anudeep Das, M. Nagappan et al. · 0 citations

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