Analyzing inconsistencies in software product lines using an ontological rule-based approach

• Published in: The Journal of systems and software 2018-03, Vol.137, p.605-617
• Main Authors: Bhushan, Megha, Goel, Shivani, Kaur, Karamjit
• Format: Article
• Language: English
• Subjects: Defects; Feature model; Inconsistency; Ontology; Rule-based approach; Software product line
• ISSN: 0164-1212; 1873-1228
• DOI: 10.1016/j.jss.2017.06.002

•Classification of feature model inconsistencies in the form of cases is proposed.•Predicate-based feature model ontology is constructed to formalize feature model.•First-order logic rules are developed to deal with inconsistencies.•Identified inconsistencies, their causes and recommend solutions to fix defects.•Improve quality of software product line to derive defect free end products. Software product line engineering (SPLE) is an evolving technical paradigm for generating software products. Feature model (FM) represents commonality and variability of a group of software products that appears within a specific domain. The quality of FMs is one of the factors that impacts the correctness of software product line (SPL). Developing FMs might also incorporate inaccurate relationships among features which cause numerous defects in models. Inconsistency is one of such defect that decreases the benefits of SPL. Existing approaches have focused in identifying inconsistencies in FMs however, only a few of these approaches are able to provide their causes. In this paper FM is formalized from an ontological view by converting model into a predicate-based ontology and defining a set of first-order logic based rules for identifying FM inconsistencies along with their causes in natural language in order to assist developers with solutions to fix defects. A FM available in software product lines online tools repository has been used to explain the presented approach and validated using 24 FMs of varied sizes up to 22,035 features. Evaluation results demonstrate that our approach is effective and accurate for the FMs scalable up to thousands of features and thus, improves SPL.