Integrating Multiple Features for Weakly-Supervised False-Passing Products Detection in Software Product Lines

Software Product Lines (SPL) enable the efficient development of configurable systems through feature modularization. However, the inherent configurability of software introduces significant challenges for fault localization within these systems. A key challenge among these is the problem of false-passing products, configurable products that contain faulty code yet coincidentally pass all their associated tests, thereby masking faults and misleading diagnosis efforts. To mitigate the negative impact of false-passing products. Supervised detection approaches are often impractical due to their reliance on complete labels, which are unavailable during early testing phases. To address this, we propose PULP, a label-agnostic detection approach that exploits the execution similarity between failing and false-passing products. PULP extracts five categories of features and employs a weakly-supervised learning algorithm to identify false-passing products without pre-labeled data. Evaluated on 823 buggy versions from six real-world SPL systems, PULP achieves superior detection performance, with best accuracy of 90.33% and precision of 94.93% for false-passing products and consistently enhances fault localization rankings after eliminating the negative impact of false-passing product. This method offers a practical tool for SPL testing and debugging in label-incomplete environments.