Large Language Models (LLMs) hold great promise in the task of code translation. However, the lack of explainability complicates the identification of the inevitable translation errors. In this paper, we propose tHinter, a debugging tool to locate translation errors in auto-translated code. The core idea of tHinter is that correctly translated, the source and translated code should present the same functionalities, giving the same output for the same input. Hence, lines in the translated code responsible for output differences are possibly translation errors. First, tHinter employs fuzzing to generate diverse test cases that thoroughly explore the translated code. Then, tHinter relies on a heuristic algorithm to pinpoint translation errors from coverage information and differential testing execution results of those test cases. This heuristic algorithm is designed to leverage both the statistics and the expertise of developers. Comprehensive experiments with real code show its effectiveness. It reduces 71% lines developers need to review during debugging and increases the likelihood of the LLM fixing translation errors in a single query by 59%. Developers generally consider it satisfactory and helpful.
Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency
Testing and Debugging Techniques for Answer Set Solver Development
Institute for Formal Models and Verification, Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校形式模型与验证研究所）；Department of Computer Science, University of Helsinki（赫尔辛基大学计算机科学系）
DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems
Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China（中国科学院神经科学研究所、脑认知与脑启发智能技术重点实验室、脑科学与智能技术卓越中心、中国科学院、上海）；University of Chinese Academy of Sciences, School of Future Technology, Beijing, 100049, China（中国科学院大学、未来技术学院、北京）；Microsoft（微软公司）
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；East China Normal University（华东师范大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Baidu Inc.（百度公司）
DDOR: Delta Debugging for Explainable Overrefusal Testing and Repair
Southeast University（东南大学）；Singapore Management University（新加坡管理学院）；Zhejiang University（浙江大学）；Huzhou Institute of Industrial Control Technology（湖州工业控制技术研究所）
TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Nanyang Technological University（南洋理工大学）；Beihang University（北京航空航天大学）；Southwest University（西南大学）
VeriPilot: An LLM-Powered Verilog Debugging Framework
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所国家专用集成电路设计实验室）；University of Chinese Academy of Sciences（中国科学院大学）；North China Electric Power University（华北电力大学）
