Fault Localization

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Overview

Fault localization aims to pinpoint where a failure originates, turning symptoms such as failing tests, agent errors, or abnormal sensor readings into actionable diagnoses. Software methods rank suspicious code using test coverage, program graphs, and learned or LLM-guided analysis, while physical-system methods use telemetry, signal patterns, and model residuals to locate faulty components. The work spans repository repair, AI agent traces, power grids, robotics, and industrial monitoring, with evaluations ranging from code-location accuracy to fault-zone identification under noise or changing conditions. A central challenge is that a plausible location may not establish causality or improve repair, and results can depend on localization granularity, available evidence, and distribution shifts.

Latest papers

CardsList

42 papers4 papers in the last four weeks, down 60% on the four weeks before. 0.0% of all new papers.

  1. On the Role of Fault Localization Context for LLM-Based Program Repair

    Apr 7, 2026Melika Sepidband, Hung Viet Pham, Hadi HemmatiFault LocalizationAutomated Program Repair

    York University, Toronto, Canada

  2. A Study on the Impact of Fault localization Granularity for Repository-Scale Code Repair Tasks

    Mar 31, 2026Joseph Townsend, Chandresh Pravin, Kwun Ho Ngan +1Repository-Level Program RepairAutomated Program Repair

    Fujitsu Research of Europe, UK

  3. SemLoc: Structured Grounding of Free-Form LLM Reasoning for Fault Localization

    Mar 31, 2026Zhaorui Yang, Haichao Zhu, Qian Zhang +2Fault LocalizationLLM Grounding

    UC Riverside · Independent Researcher · Cisco Research

  4. JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

    Mar 24, 2026Yuhui Wang, Zhixiong Yang, Ming Zhang +10LLM EvaluationMulti-Turn Dialogue Evaluation

    1Fudan University

  5. An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation

    Mar 9, 2026Mohammad Abboush, Ehab Ghannoum, Andreas RauschFault DiagnosisFault Localization

    Technische Universität Clausthal, Institute for Software and Systems Engineering, Arnold-Sommerfeld-Straße 1, Clausthal-Zellerfeld, 38678, Niedersachsen, Germany

  6. A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization

    Mar 7, 2026Nanhong Liu, Jingyi Yan, Mucun Sun +1Machine UnlearningFault Localization

    The University of Texas at Dallas, Richardson, TX, USA · Idaho National Laboratory, Idaho Falls, ID, USA

  7. Tureis: Transformer-based Unified Resilience for IoT Devices in Smart Homes

    Mar 5, 2026Alireza Borhani, Vafa Andalibi, Bahar AsgariFault DetectionFault Localization

    University of Maryland, College Park, MD, USA · Google, Manhattan, NY, USA

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

    Mar 1, 2026Zhongqiang Pan, Chuanyi Li, Wenkang Zhong +3Repository-Level Program RepairAutomated Program Repair

    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas, USA

  9. FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System

    Feb 27, 2026Kriti Thakur, Alivelu Manga Parimi, Mayukha PalPower System Fault DiagnosisFault Localization

    ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, 500084, Telangana, India · Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani- Hyderabad Campus, Hyderabad, 500078, Telangana, India

  10. Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin

    Feb 25, 2026Osimone Imhogiemhe, Yoann Jus, Hubert Lejeune +1Fault DetectionProcess Supervision

    LS2N, LS2N - équipe SIMS, Nantes Univ - ECN · Nantes Univ., Centrale Nantes LS2N, CNRS UMR 6004 F-44000 Nantes, France · Cetim +1

  11. AgentRx: Diagnosing AI Agent Failures from Execution Trajectories

    Feb 2, 2026Shraddha Barke, Arnav Goyal, Alind Khare +3Fault DiagnosisAI Agent Reliability

    1Microsoft Research · 2Microsoft · 3UIUC

  12. ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids

    Nov 7, 2025Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze +3Replay-Based Continual LearningPrototype-Based Classification

    Department of Information Engineering, University of Padova. · School of Computing, Newcastle University.