10 citations · 13 across the 10 of their papers we have counts for
11 papers
RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair
Rabeya Khatun Muna, Muhammad Ahasanuzzaman, Nakhla Rafi +3
Large language model (LLM) agents increasingly reuse prior experience, but most approaches assume that problems and solutions are already aligned. Software histories rarely provide…
Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework
Chenglin Li, Yisen Xu, Zehao Wang +3
Autonomous coding agents powered by large language models can now generate code patches directly from bug reports, but a fundamental gap remains: once a patch is produced, no mecha…
Reusing Past Repairs Through Hierarchical Trajectory Abstraction for Coding Agents
Yisen Xu, Jiayuan Zhou, Ruiqi Pan +1
Although LLM-driven repair agents can tackle complex, repository-level issues, they treat every issue independently and discard the procedural knowledge accumulated from previous r…
Preventing Premature Commitment in Coding Agents with an Evidence-Conditioned Execution Layer
Yisen Xu, Chenglin Li, Zehao Wang +2
LLM-based coding agents often edit source code or submit patches before examining enough repository evidence to justify the change, a failure pattern we call premature commitment.…
Towards Structured, State-Aware, and Execution-Grounded Reasoning for Software Engineering Agents
Tse-Hsun, Chen
Software Engineering (SE) agents have shown promising abilities in supporting various SE tasks. Current SE agents remain fundamentally reactive, making decisions mainly based on co…
SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoring
Yisen Xu, Jinqiu Yang, Tse-Hsun +1
Large Language Models (LLMs) have recently attracted wide interest for tackling software engineering tasks. In contrast to code generation, refactoring demands precise, semantics-p…
From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair
Chenglin Li, Yisen Xu, Zehao Wang +3
Repository-level automated program repair (APR) requires long-horizon reasoning over interdependent decisions. However, most LLM-based approaches reconstruct repair reasoning indep…
Evaluating Software Process Models for Multi-Agent Class-Level Code Generation
Wasique Islam Shafin, Md Nakhla Rafi, Zhenhao Li +1
Modern software systems require code that is not only functional but also maintainable and well-structured. Although Large Language Models (LLMs) are increasingly used to automate…
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…
Empowering AIOps: Leveraging Large Language Models for IT Operations Management
Arthur Vitui, Tse-Hsun Chen
The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancin…
SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents
Feng Lin, Dong Jae Kim, Tse-Husn +1
Software process models are essential to facilitate collaboration and communication among software teams to solve complex development tasks. Inspired by these software engineering…