27 citations · 91 across the 14 of their papers we have counts for
15 papers
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…
SNIPTEST: Fuzzing Multi-Level Code Slices for Validating Vulnerabilities
Aniruddhan Murali, Nobble Saji Mathews, Mahmoud Alfadel +2
Modern software systems are increasingly complex, and static analysis tools are commonly used to identify potentially vulnerable code by issuing warnings. However, these warnings o…
Functional and Secure Code Generation with Task Vectors
Felix Wang, Anudeep Das, Mei Nagappan +1
Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities. Prior work to improve the se…
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…
Using Mutation-Analysis to Examine an LLM's Ability to Summarize Code
Lara Khatib, Michael Pu, Bogdan Vasilescu +1
As developers increasingly rely on LLM-generated code summaries for documentation, testing, and review, it is important to study whether these summaries accurately reflect what the…
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…
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…
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…
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…
Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets
Partha Chakraborty, Krishna Kanth Arumugam, Mahmoud Alfadel +2
The impact of software vulnerabilities on everyday software systems is significant. Despite deep learning models being proposed for vulnerability detection, their reliability is qu…
BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learning
Partha Chakraborty, Mahmoud Alfadel, Meiyappan Nagappan
Software bugs require developers to exert significant effort to identify and resolve them, often consuming about one-third of their time. Bug localization, the process of pinpointi…
Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems
Oseremen Joy Idialu, Noble Saji Mathews, Rungroj Maipradit +2
Artificial intelligence (AI) assistants such as GitHub Copilot and ChatGPT, built on large language models like GPT-4, are revolutionizing how programming tasks are performed, rais…
LLbezpeky: Leveraging Large Language Models for Vulnerability Detection
Noble Saji Mathews, Yelizaveta Brus, Yousra Aafer +2
Despite the continued research and progress in building secure systems, Android applications continue to be ridden with vulnerabilities, necessitating effective detection methods.…
Test-Driven Development for Code Generation
Noble Saji Mathews, Meiyappan Nagappan
Recent Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements. This increasingly automated process mir…
A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)
Danielle Gonzalez, Joanna C. S. Santos, Andrew Popovich +2
Test case maintainability is an important concern, especially in open source and distributed development environments where projects typically have high contributor turnover with v…