Jul 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 45 references
Computer Science
TL;DR
CoFiLoc first performs structured bug report denoising to extract high-value technical information, and then progressively narrows the candidate space by integrating lightweight dynamic execution evidence, stack-trace-guided structural signals, and dual semantic-lexical ranking, before applying LLM-based reasoning over a compact set of fault-relevant methods.
A class-level automated multi-objective search-based system to identify and rank potentially buggy classes from bug reports, with the main objective is to maximize similarity while minimizing the number of suggested faulty files.
W. Ahmad, Mehtab Kiran Suddle, Maryam Bashir· 0 citations
A comprehensive empirical analysis of LLM-based APR techniques, focusing on how repair performance is shaped by bug complexity, fault localization, reasoning settings, and costs, reveals a nontrivial trade-off between repair effectiveness and computational cost.
Jun-Chi Liu, Ali Bigdeli, Roya Daneshi et al.· 2 citations
This paper introduces T2L (Trace-to-Line), a reproducible research framework that narrows repository-scale code into candidate vulnerable lines through AST-based chunking, structured diagnostic information collection, and evidence-guided refinement that improves trace-to-line localization.
Experiments on real-world concurrent bugs from eight large-scale Java projects show that ConFL significantly outperforms state-of-the-art IR-based and LLM-based baselines, achieving an MRR of 0.503 and a MAP of 0.486.
Shuai Shao, Dingbang Wang, Yiming Zeng et al.· 0 citations
MCR-Bench is introduced, the first defect state-aware benchmark designed for realistic multi-round code review, and in-depth error analysis dissects the distinct drivers of false positives and false negatives, revealing critical weaknesses such as cross-round temporal misalignment and inadequate long-range memory.
De-Wu Zheng, Yan-Lin Wang, Xi-Wen Wang et al.· Proceedings of the ACM on So...· 1 citation
LISA iteratively generates API sequences and program invariants guided by API n-gram feedback, achieving higher bug-detection rates and competitive code coverage compared with both fuzzing and prior LLM-based test generation approaches, and reporting each finding as a high-confidence bug candidate for developer confirm...
