#fault-localization News & Analysis

4 articles tagged with #fault-localization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Jun 237/10

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Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale

Holmes is a multi-agent AI system that automates root cause analysis for mobile app crashes in large-scale production environments by synthesizing runtime signals like stack traces and logs without requiring local reproduction. Deployed at WeChat, it achieves 87.6% accuracy in fault localization and reduces debugging time from hours to 77 seconds, demonstrating practical AI applications in enterprise software reliability.

AINeutralarXiv – CS AI · Jun 236/10

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Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs

Researchers present a human-in-the-loop framework combining fine-tuned small language models with knowledge graphs to automatically detect and repair semantic errors in SysML v2 models that bypass traditional compiler validation. The approach achieves over 91% repair accuracy using domain-specific training data and generates practical repair suggestions for engineer review.

AINeutralarXiv – CS AI · Jun 26/10

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Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

Researchers introduce LinuxFLBench, a fault localization benchmark for Linux kernel bugs, and demonstrate that current LLM agents struggle with this complex task, achieving only 41.6% accuracy. They propose LinuxFL+, an enhancement framework that improves accuracy by 7.2-11.2% across all tested agents, addressing a critical gap in software debugging automation.

AIBullisharXiv – CS AI · Mar 36/107

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RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair

RepoRepair is a new AI-powered automated program repair system that uses hierarchical code documentation to fix bugs across entire software repositories. The system achieves a 45.7% repair rate on SWE-bench Lite at $0.44 per fix by leveraging LLMs like DeepSeek-V3 and Claude-4 for fault localization and code repair.