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.
This work was supported by Science Foundation Ireland grants 10/CE/I1855 and 13/RC/2094.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Available here: http://hibernia.ucd.ie/EvolvingFMs/.
References
Alshahwan, N., Harman, M.: Automated web application testing using search based software engineering. In: ASE, pp. 3–12 (2011)
Fraser, G., Arcuri, A.: The seed is strong: seeding strategies in search-based software testing. In: ICST, pp. 121–130 (2012)
Harman, M., Jia, Y., Krinke, J., Langdon, W.B., Petke, J., Zhang, Y.: Search based software engineering for software product line engineering: a survey and directions for future work. In: SPLC, pp. 5–18 (2014)
Henard, C., Papadakis, M., Harman, M., Le Traon, Y.: Combining multi-objective search and constraint solving for configuring large software product lines. In: ICSE, pp. 517–528 (2015)
Metzger, A., Pohl, K.: Software product line engineering, variability management: achievements and challenges. In: FSE, pp. 70–84 (2014)
Pleuss, A., Botterweck, G., Dhungana, D., Polzer, A., Kowalewski, S.: Model-driven support for product line evolution on feature level. JSS 85(10), 2261–2274 (2012)
Pohl, R., Lauenroth, K., Pohl, K.: A performance comparison of contemporary algorithmic approaches for automated analysis operations on feature models. In: ASE, pp. 313–322 (2011)
Sayyad, A.S., Ingram, J., Menzies, T., Ammar, H.: Scalable product line configuration: a straw to break the camel’s back. In: ASE, pp. 465–474 (2013)
She, S.: Feature Model Synthesis. Ph.d thesis, University of Waterloo (2013)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Brevet, D., Saber, T., Botterweck, G., Ventresque, A. (2016). Preliminary Study of Multi-objective Features Selection for Evolving Software Product Lines. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-47106-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47105-1
Online ISBN: 978-3-319-47106-8
eBook Packages: Computer ScienceComputer Science (R0)