LogHound: Coverage-Aware Path Reconstruction for Log-Based Fault Localization
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.