From the 1 of 7 linked papers with an AI index.
7 papers
SemaDiff: Identifying Semantic-Changing Commits with Generated Code and Tests
Maha Ayub, Michael Konstantinou, Ahmed Khanfir +2
The paper introduces SemaDiff, a method that uses large language models to generate additional calling code and tests in order to compare the behavior of pre- and post‑commit versi…
AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
Yuecai Zhu, Nikolaos Tsantalis, Peter C. Rigby
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. Thi…
Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring
Abhiram Bellur, Fraol Batole, Mohammed Raihan Ullah +12
MOVEMETHOD is a hallmark refactoring. Despite a plethora of research tools that recommend which methods to move and where, these recommendations do not align with how expert develo…
MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration
Yisen Xu, Feng Lin, Jinqiu Yang +3
Maintaining and scaling software systems relies heavily on effective code refactoring, yet this process remains labor-intensive, requiring developers to carefully analyze existing…
Refactoring-aware Block Tracking in Commit History
Mohammed Tayeeb Hasan, Nikolaos Tsantalis, Pouria Alikhanifard
Tracking statements in the commit history of a project is in many cases useful for supporting various software maintenance, comprehension, and evolution tasks. A high level of accu…
An Empirical Study of Refactoring Engine Bugs
Haibo Wang, Zhuolin Xu, Huaien Zhang +2
Refactoring is a critical process in software development, aiming at improving the internal structure of code while preserving its external behavior. Refactoring engines are integr…