Paper Info
Title
Preliminary Study of Multi-objective Features Selection for Evolving Software Product Lines.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-47106-8_23
Lecture Notes in Computer Science
Keywords
Field
DocType
SPL,Multi-objective,Genetic algorithm,Evolution
Software engineering,Computer science,Software,Genetic algorithm
Conference
Volume
ISSN
Citations 
9962
0302-9743
1
PageRank 
References 
Authors
0.35
8
4
Name
Order
Citations
PageRank
David Brevet110.35
Takfarinas Saber2254.90
Goetz Botterweck362046.72
Anthony Ventresque410817.08