FOFL: Dynamic Function Output-Based Software Fault Localization via Deep Learning

Learning-based fault localization has become a prominent research direction in software engineering due to its ability to leverage diverse program artifacts such as execution traces and coverage data for precise fault identification. However, current techniques face two fundamental limitations: (1) oversimplified representations of program behavior through basic coverage metrics, and (2) high computational cost associated with collecting fine-grained runtime data. In this work, we present FOFL, a novel function output-based fault localization approach. Our intuition is that programs can be modeled as complex dynamical systems, where faults manifest as perturbations observable through function outputs. Our method first captures function-level output patterns during execution, then encodes them into a structured matrix representation that preserves system-level behavioral signatures. These matrices are analyzed through a hybrid deep learning architecture combining convolutional neural networks for spatial feature extraction and attention mechanisms for critical pattern recognition, ultimately producing a ranked list of suspicious locations. Experiments on the widely used Defects4J benchmark show that FOFL outperforms existing techniques by localizing 204 bugs in the Top-1 ranking, which corresponds to a 25-bug improvement over state-of-the-art methods, while also enhancing MFR and MAR by 4.5% and 4.3%, respectively. Furthermore, ablation studies confirm the positive contribution of listwise loss function and feature aggregation design. The results establish FOFL as an effective solution that advances fault localization through principled system modeling and targeted learning of informative data representations.