Explainable Fault Localization in Neural-Augmented Codebases

Abstract

Modern software development increasingly integrates neural models such as code completion engines, automated refactoring systems, and learned optimization modules into traditional codebases, creating hybrid environments commonly referred to as neural-augmented codebases. While these systems achieve significant productivity gains, debugging them presents unique challenges due to the opaque reasoning processes of neural components. Conventional fault localization techniques are insufficient, as they cannot effectively attribute errors originating from model-generated code, neural decision boundaries, or interactions between learned and symbolic components. This paper proposes an explainable fault localization framework that combines program analysis, runtime tracing, and interpretable machine learning techniques to identify, rank, and justify the root causes of failures in neural-augmented codebases. By integrating explainability mechanisms such as attention heatmaps, causal dependency graphs, and interpretable embeddings, the framework enhances developer trust, reduces debugging cost, and provides actionable diagnostic insights. Experimental evaluation on real-world hybrid systems demonstrates improved fault detection accuracy, reduced false positives, and higher interpretability scores compared to state-of-the-art approaches. The results show that explainability is not merely an auxiliary feature, but a critical enabler of scalable, safe adoption of neural components in modern software engineering workflows.