Poster: Quantification of Feature-Interaction Masking in JHipster

Configurable software systems, such as software product lines, enable the generation of products based on configurations tailored to specific requirements by combining reusable features. A key challenge in product lines lies in combinatorial interaction testing, which ensures that all possible feature combinations are tested to identify configurations that may fail. When a configuration fails, pinpointing the feature or the feature interaction causing the fault is crucial. However, fault masking - where faulty interactions remain undetected because other features or interactions could override their effects - potentially hinders the effective identification of faults in product lines. Despite the potential of missing critical interaction faults, fault masking in product lines has received limited attention in existing research. To address this gap, we investigate and analyze on already identified faults of the real-world product line JHipster and quantitatively analyze these faults in terms of masking. In our case study, we find evidence of the existence of fault masking in JHipster and how the detectability of masked faults is influenced. For one feature-interaction fault in JHipster, we miss to identify 17.6% of all configurations containing this fault due to masking effects. By analyzing masked faults of a real-world product line, we raise awareness of investigating feature-interaction masking further in software product lines.