一句话：提出 HypoDeduce，用溯因推理生成并评估竞争假设，缓解 LLM 故障定位早期剪枝备选假设的问题。
故障定位是调试的关键环节，近期工作利用大语言模型从失败的测试用例中定位缺陷方法，其中 SoapFL 采用结构化的多阶段流程：先依据对测试失败的分析锁定可疑的类，再进一步确定可疑方法。本文对该多阶段流程做漏斗分析，指出一个核心局限——早期就选定单一假设会导致有用的备选假设被过早丢弃。受开发者会同时权衡多个竞争性根因假设这一行为启发，作者提出原型系统 HypoDeduce，将溯因推理引入故障定位：生成多个竞争假设并对其逐一评估，跨类与方法层面整合证据，最终据此识别可疑方法。该工作属于神经符号（LLM 与符号推理结合）故障定位方向，摘要未给出具体实验指标。
Fault localization is essential for debugging. Recently, Large Language Model (LLM)-based techniques have been proposed to identify buggy methods from failing test cases. The state-of-the-art approach, SoapFL, adopts a structured multi-phase procedure. Given a test failure, suspicious classes and methods are identified based on its analysis of the test failure. We conduct a funnel analysis of the multi-phase approach. While effective, our analysis highlights a limitation: the early selection of a single hypothesis can cause useful alternatives to be discarded. Motivated by how developers consider multiple competing hypotheses of the root cause of a bug as they analyze the evidence, we prototype HypoDeduce, which applies abductive reasoning. HypoDeduce generates and evaluates competing hypotheses, integrates evidence across classes and methods, and then identifies suspicious methods.