posted on 2017-02-09, 16:11authored byDavid 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