activity
20242026
collaborators

7 papers

cs.SE2026

COMMITGUARD: Differential Slice Fuzzing for Commit-Induced Bug Detection

Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel +1

Modern software systems evolve through frequent commits that implement bug fixes, features, and security patches. Although code review and testing are widely used to check these ch…

cs.SE2026

What Makes a Good Bug Report for an AI Agent?

Lara Khatib, Noble Saji Mathews, Meiyappan Nagappan +2

Automated program repair (APR) agents are transitioning from research benchmarks to developer workflows, yet they still begin with bug reports written for human developers. While d…

cs.SE2026

AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests

Lara Khatib, Noble Saji Mathews, Meiyappan Nagappan

Bug reproduction is critical in the software debugging and repair process, yet the majority of bugs in open-source and industrial settings lack executable tests to reproduce them a…

cs.SE2025

Does SWE-Bench-Verified Test Agent Ability or Model Memory?

Thanosan Prathifkumar, Noble Saji Mathews, Meiyappan Nagappan

SWE-Bench-Verified, a dataset comprising 500 issues, serves as a de facto benchmark for evaluating various large language models (LLMs) on their ability to resolve GitHub issues. B…

cs.SE2025

Is Your Automated Software Engineer Trustworthy?

Noble Saji Mathews, Meiyappan Nagappan

Large Language Models (LLMs) are being increasingly used in software engineering tasks, with an increased focus on bug report resolution over the past year. However, most proposed…

cs.SE2024

Design choices made by LLM-based test generators prevent them from finding bugs

Noble Saji Mathews, Meiyappan Nagappan

There is an increasing amount of research and commercial tools for automated test case generation using Large Language Models (LLMs). This paper critically examines whether recent…