27 citations · 96 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
CodeSAM: Source Code Representation Learning by Infusing Self-Attention with Multi-Code-View Graphs
Alex Mathai, Kranthi Sedamaki, Debeshee Das +4
Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is l…
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.…
FuzzSlice: Pruning False Positives in Static Analysis Warnings Through Function-Level Fuzzing
Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel +2
Manual confirmation of static analysis reports is a daunting task. This is due to both the large number of warnings and the high density of false positives among them. Fuzzing tech…
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…