20 papers · 1 filter
Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software
Fanyu Wang, Chetan Arora, Zhenping Xie +4
LLM-based software (LBS) integrates large language models as core components to deliver flexible, personalised responses. Unlike traditional software with deterministic outputs, LB…
CodeChat-Eval: Evaluating Large Language Models in Multi-Turn Code Refinement Dialogues
Guoxiang Guo, Kla Tantithamthavorn, Neelofar Neelofar +2
Large Language Models (LLMs) are increasingly used in software engineering to generate and refine code. In practice, developers often continue from an initial code generation reque…
MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems
Aaron Guoxiang Guo, Aldeida Aleti, Neelofar Neelofar +3
With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced meth…
Trustworthy AI Software Engineers
Aldeida Aleti, Baishakhi Ray, Rashina Hoda +1
With the rapid rise of AI coding agents, the fundamental premise of what it means to be a software engineer is in question. In this vision paper, we examine what it means for an AI…
UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
Lam Nguyen Tung, Xiaoning Du, Neelofar Neelofar +1
Machine learning (ML) has shown promise in vulnerability detection, but ML detectors may rely on irrelevant code features, causing them to highlight non-vulnerable lines as suspici…
ViBR: Automated Bug Replay from Video-based Reports using Vision-Language Models
Sidong Feng, Dingbang Wang, Nikola Tomic +3
Bug reports play a critical role in software maintenance by helping users convey encountered issues to developers. Recently, GUI screen capture videos have gained popularity as a b…