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Preliminary study of multi-objective features selection for evolving software product lines

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conference contribution
posted on 2017-02-09, 16:11 authored by David Brevet, Takfarinas Saber, Goetz Botterweck, Anthony Ventresque
When dealing with software-intensive systems, it is often beneficial to consider families of similar systems together. A common task is then to identify the particular product that best fulfils a given set of desired product properties. Software Product Lines Engineering (SPLE) provides techniques to design, implement and evolve families of similar systems in a systematic fashion, with variability choices explicitly represented, e.g., as Feature Models. The problem of picking the ‘best’ product then becomes a question of optimising the Feature Configuration. When considering multiple properties at the same time, we have to deal with multi-objective optimisation, which is even more challenging. While change and evolution of software systems is the common case, to the best of our knowledge there has been no evaluation of the problem of multi-objective optimisation of evolving Software Product Lines. In this paper we present a benchmark of large scale evolving Feature Models and we study the behaviour of the state-of-the-art algorithm (SATIBEA). In particular, we show that we can improve both the execution time and the quality of SATIBEA by feeding it with the previous configurations: our solution converges nearly 10 times faster and gets an 113% improvement after one generation of genetic algorithm.

History

Publication

8th International Symposium on Search Based Software Engineering, SSBSE: Lecture Notes in Computer Science (LNCS);9962, pp. 274-280

Publisher

Springer

Note

peer-reviewed

Other Funding information

SFI

Rights

The original publication is available at www.springerlink.com

Language

English

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