Agentic systems have been gaining traction in solving software engineering tasks. These tasks span from writing documentation, fixing faults in the codebase, and developing new features. A promising application of LLM agents is addressing software “issues”, with an issue capturing a unit of improvement needed in a software project. Issues can be detected and constructed by static analysis tools, such as SonarQube. Static analysis tools frequently generate a substantial number of reports related to security vulnerabilities and code quality, imposing a significant manual workload on developers. With the advances in agentic AI, there is potential to automatically remediate these issues, thereby reducing developer effort.
In this paper, we present our experience and lessons learned in adapting the AutoCodeRover program improvement agent to automatically propose patches for issues reported by SonarQube. We name this new agent SonarQube Remediation Agent, specialized for fixing SonarQube issues. SonarQube Remediation Agent is designed to be capable of interacting with mission-critical codebases in a secure and trustworthy manner. We discuss our approach in tackling practical challenges such as handling large volumes of issues and designing seamless user interactions. SonarQube Remediation Agent is integrated into the software development lifecycle by suggesting patches during the pull request review workflow, enabling developers to efficiently improve software quality and security with SonarQube.
Tue 7 JulDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Program RepairIdeas, Visions and Reflections / Research Papers / Industry Papers at MB 3.270 Chair(s): Lwin Khin Shar Singapore Management University | ||
11:00 20mTalk | Automated Repair of TEE Partitioning Issues via DSL-Guided and LLM-Assisted Patching Research Papers Chengyan Ma Singapore Management University, Jieke Shi Singapore Management University, Ruidong Han Singapore Management University, Ye Liu Singapore Management University, FENG Li , Yuqing Niu , David Lo Singapore Management University Pre-print | ||
11:20 20mTalk | TLR: Codebase-Level C Memory Management Error Repair with Large Language Models Research Papers Xiao Cheng Macquarie University, Zhihao Guo UTS, Huan Huo University of Technology Sydney, Yulei Sui University of New South Wales Pre-print | ||
11:40 20mTalk | AutoCodeRover: Agentic Program Repair for SonarQube Issues Industry Papers Martin Mirchev National University of Singapore, Ridwan Salihin Shariffdeen SonarSource, Haifeng Ruan National University of Singapore, Yuntong Zhang National University of Singapore, Abhik Roychoudhury National University of Singapore DOI Pre-print | ||
12:00 10mTalk | Who Wrote This Patch? Toward Accountable Automated Program Repair Ideas, Visions and Reflections Huaijin Ran Xi’an Jiaotong-Liverpool University, Haoyi Zhang Xi’an Jiaotong-Liverpool University, Kisub Kim DGIST, Xunzhu Tang University of Luxembourg DOI | ||
12:10 20mTalk | Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors Research Papers Jiawei Shen East China Normal University, Chengcheng Wan East China Normal University, Ruoyi Qiao East China Normal University, Jiazhen Zou East China Normal University, Hang Xu East China Normal University, Yuchen Shao East China Normal University, Shanghai Innovation Institute, Yueling Zhang East China Normal University, Weikai Miao East China Normal University, Geguang Pu East China Normal University, China Pre-print | ||