Hyper Explanations for Feature-Model Defect Analysis

Proprietary formats, missing analysis tools, the lack of continuous toolchains and their complexity itself impair the maintainability of industrial variability models, making them prone to defects. Also, automated analysis of variability models is still not common in industry. To gain detailed information about the defects in a proprietary variability model, it can be converted into a standardized feature model to apply automated analyses that expose present defects. In this paper, however, we exclusively handle and evaluate defects of type dead feature. Resolving those defects can be challenging, as their cause can be complex, especially for large feature models. To mitigate this, an explanation can be generated which identifies feature model parts that are involved in a specific defect. Although those explanations provide valuable information, handling a high number of defects at the same time remains a tedious task, as there is no prioritization that states which defect is to be handled first to achieve the best progress. In this paper, we propose a concept to automatically derive that prioritization by deriving a hyper explanation that aggregates the information provided by the explanations of all individual defects. Hyper explanations allow to derive a prioritization not only for defects but also for defect-creating constraints. We apply our concept to industrial variability models and discussed the results with domain experts, which leads to an unexpected conclusion: The models contain intentionally created dead features. We further evaluate the usage of intentionally dead features in industry and discuss how to handle them together with actual defects.