Leveraging Feature Similarity for Earlier Detection of Unwanted Feature Interactions in Evolving Software Product Lines

Abstract

Software product lines enable reuse of shared software across a family of products. As new products are built in the product line, new features are added. The features are units of functionality that provide services to users. Unwanted feature interactions, wherein one feature interferes with another feature’s operation, is a significant problem, especially as large software product lines evolve. Detecting feature interactions is a time-consuming and difficult task for developers. Moreover, feature interactions are often only discovered during testing, at which point costly re-work is needed. This paper proposes a similarity-based method to identify unwanted feature interactions much earlier in the development process. It uses knowledge of prior feature interactions stored with the software product line’s feature model to help find unwanted interactions between a new feature and existing features. The paper describes the framework and algorithms used to detect the feature interactions using three path similarity measures and evaluates the approach on a real-world, evolving software product line. Results show that the approach performs well, with 83% accuracy and 60% to 100% coverage of feature interactions in experiments, and scales to a large number of features.