27 citations · 91 across the 14 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…
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…