91 citations · 95 across the 14 of their papers we have counts for
5 papers · 2 filters
Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization
Md Nakhla Rafi, Dong Jae Kim, Tse-Hsun Chen +1
Large Language Models (LLMs) show great promise in software engineering tasks like Fault Localization (FL) and Automatic Program Repair (APR). This study investigates the impact of…
Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
Pengfei He, Shaowei Wang, Shaiful Chowdhury +1
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge bases, achieving state-of-the-art results in various coding tasks. The…
A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion
Md Nakhla Rafi, Dong Jae Kim, Tse-Hsun Chen +1
Identifying and resolving software faults remains a challenging and resource-intensive process. Traditional fault localization techniques, such as Spectrum-Based Fault Localization…
Towards Better Graph Neural Network-based Fault Localization Through Enhanced Code Representation
Md Nakhla Rafi, Dong Jae Kim, An Ran Chen +2
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization,…
LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing
Zeyang Ma, An Ran Chen, Dong Jae Kim +2
Logs are important in modern software development with runtime information. Log parsing is the first step in many log-based analyses, that involve extracting structured information…