3 papers
cs.SE2025
Crash Report Enhancement with Large Language Models: An Empirical Study
S M Farah Al Fahim, Md Nakhla Rafi, Zeyang Ma +3
Crash reports are central to software maintenance, yet many lack the diagnostic detail developers need to debug efficiently. We examine whether large language models can enhance cr…
cs.SE2025
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin, Dong Jae Kim, Zhenhao Li +3
When using LLMs to address Non-Functional Requirements (NFRs), developers may behave differently (e.g., expressing the same NFR in different words). Robust LLMs should output consi…
cs.SE2024
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…