Jul 2026· Fall Joint Computer Conference· pp. 121-128· 0 citations· 34 references
Abstract
In large-scale systems, fault localization remains expensive because bug reports are often ambiguous and incomplete. In practice, developers rely heavily on runtime logs and coverage data as critical clues for reasoning about how faults propagate through systems. However, these rich diagnostic signals are rarely integrated systematically into automated localization frameworks. To address this gap, we propose LogHound, a practical debugging assistant that combines call-graph-based execution path reconstruction with static coverage estimates to rank suspicious program entities. We evaluate LogHound across 5 representative distributed systems, comparing it against recent baselines including COCA, ReAct, and RCACopilot. The results show that LogHound consistently outperforms prior approaches, particularly in Top-3 and Top-5 accuracy. An ablation study further reveals that execution path reconstruction is essential for recovering causal chains of failures, while coverage scores provide complementary evidence that sharpens the ranking. These findings validate our design choices and highlight the importance of combining multiple forms of diagnostic clues. By reducing manual investigation costs and accelerating debugging cycles, LogHound contributes to enhancing the reliability of long-lived, evolving software systems.
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.
Nham Cao, Nhut Tien Nguyen, Thanh Nguyen· International Conference on...· 0 citations
Large software systems often suffer from time inefficiencies that cause excessive execution time despite functional correctness. Localizing their fix locations is difficult because, unlike functional bugs, they produce neither test failures nor stack-trace clues, making traditional and recent LLM-based fault localization methods unsuitable. Runtime profiling provides alternative evidence but faces three challenges in repository-level settings: single-run profiling cannot reliably distinguish inefficiency hotspots from execution noise; existing profilers struggle to extract relevant execution paths from extensive background execution; and a semantic gap remains between observed hotspots and actual fix locations. We propose EffiHolmes, an LLM-based framework for repository-level time inefficiency fix localization. EffiHolmes uses differential profiling under default and scaled workloads to identify inefficiency hotspots, extracts compact execution paths connecting these hotspots to the reported inefficient function, and employs domain-guided LLM reasoning to locate the underlying inefficiency logic. We also introduce RepoEffi-Bench, the first benchmark for repository-level inefficiency localization, containing 140 high-quality issues collected from popular Python repositories. Experiments show that EffiHolmes consistently outperforms state-of-the-art retrieval-, agent-, and profiling-based baselines, improving file-level Acc@3 by 4.29 percentage points with GPT-5.1 and function-level Acc@5 by 15.00 percentage points with qwen3-4b. It also remains robust across model capacities.
The proposed IssueExec bridges the semantic gap through domain-knowledge-enhanced test representations and filters noise via hierarchical trace analysis, which bridges the semantic gap through domain-knowledge-enhanced test representations and filters noise via hierarchical trace analysis.
Jiawei Liu, Yun Lin, Chenyan Liu et al.· arXiv.org· 0 citations
This paper investigates automated fault localization for verification-aware languages by comparing two paradigms: state-based and counterexample-based localization, and shows that counterexample-based approaches substantially outperform state-based localization in this setting.
Álvaro F. Silva, Isabel Amaral, João Pascoal Faria et al.· 0 citations
Overall, TraceGate shows that rethinking debugging through controlled observability, rather than relying solely on stronger models or larger prompts, can make LLM-assisted repair more effective, efficient and controllable.
Nicolas Schuler, MateVincenzoScotti, RaffaelaMirandola· 0 citations
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