Abstract
Software product lines enable the reuse of shared software across a family of products. As new products are built in the product line, new features are added. A feature is a unit of functionality. Unwanted feature interactions, wherein one feature hinders another feature’s operation, are 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. The work described here investigates how to discover feature interactions much earlier in the development process. Toward this goal, we propose a similarity-based approach that mines prior feature interactions stored in the software product line’s artifacts to predict unwanted interactions between a new feature and existing features. Initial results show that the planned methodology performs well in terms of accuracy and coverage both in experiments on three small software product lines in the literature and in experiments on one large, real-world software product line.
Supervised by: Dr. R. R. Lutz.
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Acknowledgments
The work in this paper was partially funded by National Science Foundation Grant CCF 1513717.
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Khoshmanesh, S., Lutz, R.R. (2019). Feature Similarity: A Method to Detect Unwanted Feature Interactions Earlier in Software Product Lines. In: Amato, G., Gennaro, C., Oria, V., Radovanović , M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science(), vol 11807. Springer, Cham. https://doi.org/10.1007/978-3-030-32047-8_32
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DOI: https://doi.org/10.1007/978-3-030-32047-8_32
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