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

Author
Year of Publication
2016
Conference
Search Based Software Engineering - 8th International Symposium, SSBSE 2016, Raleigh, NC, USA, October 8-10, 2016, Proceedings
Pages
274-280
URL
https://doi.org/10.1007/978-3-319-47106-8_23
DOI
10.1007/978-3-319-47106-8_23
Keywords
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.