A Low-Cost Feature Interaction Fault Localization Approach for Software Product Lines

In Software Product Lines (SPLs), localizing buggy feature interactions helps developers identify the root cause of test failures, thereby reducing their workload. This task is challenging because the number of potential interactions grows exponentially with the number of features, resulting in a vast search space, especially for large SPLs. Previous approaches have partially addressed this issue by constructing and examining potential feature interactions based on suspicious feature selections (e.g., those present in failed configurations but not in passed ones). However, these approaches often overlook the causal relationship between buggy feature interaction and test failures, resulting in an excessive search space and high-cost fault localization. To address this, we propose a low-cost Counterfactual Reasoning-Based Fault Localization (CRFL) approach for SPLs, which enhances fault localization efficiency by reducing both the search space and redundant computations. Specifically, CRFL employs counterfactual reasoning to infer suspicious feature selections and utilizes symmetric uncertainty to filter out irrelevant feature interactions. Additionally, CRFL incorporates two findings to prevent the repeated generation and examination of the same feature interactions. We evaluate the performance of our approach using eight publicly available SPL systems. To enable comparisons on larger real-world SPLs, we generate multiple buggy mutants for both BerkeleyDB and TankWar. Experimental results show that our approach reduces the search space by 51%∼73% for small SPLs (with 6∼9 features) and by 71%∼88% for larger SPLs (with 13∼99 features). The average runtime of our approach is approximately 15.6 times faster than that of a state-of-the-art method. Furthermore, when combined with statement-level localization techniques, CRFL can efficiently localize buggy statements, demonstrating its ability to accurately identify buggy feature interactions.