450 citations · 883 across the 31 of their papers we have counts for
10 papers · 1 filter
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
Juyeon Yoon, Robert Feldt, Shin Yoo
GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case s…
Large Language Models for Software Engineering: Survey and Open Problems
Angela Fan, Beliz Gokkaya, Mark Harman +4
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LL…
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…
Just-in-Time Flaky Test Detection via Abstracted Failure Symptom Matching
Gabin An, Juyeon Yoon, Thomas Bach +2
We report our experience of using failure symptoms, such as error messages or stack traces, to identify flaky test failures in a Continuous Integration (CI) pipeline for a large in…
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…
The Inversive Relationship Between Bugs and Patches: An Empirical Study
Jinhan Kim, Jongchan Park, Shin Yoo
Software bugs pose an ever-present concern for developers, and patching such bugs requires a considerable amount of costs through complex operations. In contrast, introducing bugs…
Repairing DNN Architecture: Are We There Yet?
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems, software developers are increasingly required to design, train, and deploy such models into…