Intent-Aware Defect Pattern Extraction from Singular Examples

As software systems grow in complexity, particularly in safety-critical domains, detecting defects early is crucial to prevent severe failures. Among existing automated defect detection methods, rule-based systems excel at catching well-understood mistakes but are hard to generalize beyond their predefined rules. To address this challenge, we introduce DslGen, a novel approach that automatically extracts high-quality defect patterns from singular defect repair examples by leveraging large language models (LLMs). Specifically, it first infers the semantic intent of a defect repair for capturing the defect’s essential characteristics via LLM-powered analysis, then generates precise constraint rules to define the defect pattern. Unlike prior methods, DslGen does not require numerous examples or rigid abstraction schemes, enabling robust generalization even from a single fix. To evaluate the effectiveness of DslGen, we evaluated it in two distinct scenarios: (1) controlled experiments and (2) a simulated real-world defect detection pipeline. In the controlled setting, results show that DslGen outperforms all baselines, achieving a precision up to 81.1% and a recall of over 73.0%. In the simulated scenario, DslGen also performed best with an average improvement of 425.1% over state-of-the-art competitors (including GPT-4o). A user study with 15 experienced developers further confirmed DslGen’s practicality, with participants praising the readability and generalizability of its generated DSLs and expressing strong adoption intent.