104 citations · 201 across the 20 of their papers we have counts for
6 papers · 1 filter
Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction
Sungmin Kang, Juyeon Yoon, Nargiz Askarbekkyzy +1
Bug reproduction is a critical developer activity that is also challenging to automate, as bug reports are often in natural language and thus can be difficult to transform to test…
The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications
Jae Yong Lee, Sungmin Kang, Juyeon Yoon +1
Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering app…
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
Sungmin Kang, Gabin An, Shin Yoo
Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce dev…
Towards Autonomous Testing Agents via Conversational Large Language Models
Robert Feldt, Sungmin Kang, Juyeon Yoon +1
Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveri…
Towards Objective-Tailored Genetic Improvement Through Large Language Models
Sungmin Kang, Shin Yoo
While Genetic Improvement (GI) is a useful paradigm to improve functional and nonfunctional aspects of software, existing techniques tended to use the same set of mutation operator…
Explainable Automated Debugging via Large Language Model-driven Scientific Debugging
Sungmin Kang, Bei Chen, Shin Yoo +1
Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with exist…