Abstract
Recent research on software product line engineering has led to several search-based frameworks for reverse engineering feature models. The most common fitness function utilized maximizes the number of matched products with an oracle set of products. However, to calculate this fitness each product defined by the chromosome has to be enumerated using a SAT solver and this limits scalability to product lines with fewer than 30 features. In this paper we propose \(\textit{SAT}_{\textit{ff}}\), a fitness function that simulates validity by computing the difference between constraints in the chromosome and oracle. In an empirical study on 101 feature models comparing \(\textit{SAT}_{\textit{ff}}\) with two existing fitness functions that use the enumeration technique we find that \(\textit{SAT}_{\textit{ff}}\) shows a significant improvement over one, and no significant difference with the other one. We also find that \(\textit{SAT}_{\textit{ff}}\) requires only 7 % of the runtime on average scaling to feature models with as many as 97 features.
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This research was supported in part by the National Science Foundation under award number CCF-1161767.
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Thianniwet, T., Cohen, M.B. (2016). Scaling up the Fitness Function for Reverse Engineering Feature Models. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_9
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DOI: https://doi.org/10.1007/978-3-319-47106-8_9
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