works on

From the 1 of 9 linked papers with an AI index.

collaborators

9 papers

cs.SE2026

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

Haomin Qi, Xingliang Wang, Xuanqi Gao +9

The paper introduces Change2Task, a system that turns merged pull requests from software repositories into verified, executable coding‑agent tasks by reconstructing the code state…

cs.SE2026

DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes

Shenghao Yang, Bo Lu, Yaochen Liu +5

Modern software projects depend on numerous third-party libraries, whose updates often introduce breaking changes. Adapting consumer code to such changes remains labor-intensive an…

cs.SE2026

AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation

Chenyu Zhao, Shenglin Zhang, Wenwei Gu +5

Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent re…

cs.SE2026

Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents

Chenyu Zhao, Shenglin Zhang, Yihang Lin +7

Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose t…

cs.SE2026

EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair

Chenyu Zhao, Minghua Ma, Shenglin Zhang +5

Frequent toolchain updates and growing ISA diversity have made system-level software package repair increasingly important. Diagnosing and repairing build failures remains challeng…

cs.SE2026

Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems

Chenyu Zhao, Shenglin Zhang, Zeshun Huang +8

Large language models (LLMs) have shown growing potential in software engineering, yet few benchmarks evaluate their ability to repair software during migration across instruction…