Preliminary Study of Multi-objective Features Selection for Evolving Software Product Lines

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Title: Preliminary Study of Multi-objective Features Selection for Evolving Software Product Lines
Authors: Brevet, David
Saber, Takfarinas
Botterweck, Goetz
Ventresque, Anthony
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Date: 24-Sep-2016
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Volume: 9962
Start page: 274
End page: 280
Keywords: SPLMulti-objectiveGenetic algorithmEvolution
DOI: 10.1007/978-3-319-47106-8_23
Language: en
Status of Item: Peer reviewed
Conference Details: 18th International Symposium on Search Based Software Engineering (SSBSE 2016), Ralaigh, North Carolina, USA, 8-10 October 2016
Appears in Collections:Computer Science Research Collection
PEL Research Collection

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