ConFL: Explainable Concurrent Fault Localization via Hierarchy-Guided LLM Reasoning
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
Localizing concurrent bugs from bug reports alone is challenging due to incomplete information, misleading program-entity mentions, and complex cross-thread interactions, causing existing LLM-based approaches to suffer from unstable reasoning and limited explainability. We propose ConFL, an explainable concurrent fault localization framework that augments LLM reasoning with structured concurrency knowledge. ConFL constructs a Concurrent Knowledge Base (CKB) from source code and performs LLM-guided hierarchical retrieval to progressively narrow the search space from components to interaction-level concurrency contexts. An interaction-level DSL explicitly encodes cross-thread interactions over shared resources, enabling focused reasoning without traversing deep call chains. 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, while remaining robust to noisy bug reports, unseen bugs, and different LLM backbones.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 3 of its 32 references that are in the map. First posted on arXiv 4 Aug 2026.
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- 2025Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models
- 2025Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework ↗
- 2025Exploring the Potential and Limitations of Large Language Models for Novice Program Fault Localization ↗
- 2026Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization ↗
- 2026Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads
- 2026RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models