Institutions | About Us | Help | Gaeilge
rian logo


Mark
Go Back
MILPIBEA: Algorithm for multi-objective features selection in (evolving) software product lines
Saber, Takfarinas; Brevet, David; Botterweck, Goetz; Ventresque, Anthony
Software Product Lines Engineering (SPLE) proposes techniques to model, create and improve groups of related software systems in a systematic way, with different alternatives formally expressed, e.g., as Feature Models. Selecting the ‘best’ software system(s) turns into a problem of improving the quality of selected subsets of software features (components) from feature models, or as it is widely known, Feature Configuration. When there are different independent dimensions to assess how good a software product is, the problem becomes even more challenging – it is then a multi-objective optimisation problem. Another big issue for software systems is evolution where software components change. This is common in the industry but, as far as we know, there is no algorithm designed to the particular case of multi-objective optimisation of evolving software product lines. In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).
Keyword(s): software product line; feature selection; multi-objective optimisation; evolutionary algorithm; mixed-integer linear programming
Publication Date:
2020
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): 13RC2094
Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science, Paquete L., Zarges C. (eds);10102
https://doi.org/10.1007/978-3-030-43680-3_11
13/RC/2094
Publisher(s): Springer
First Indexed: 2020-06-07 06:29:33 Last Updated: 2020-06-07 06:29:33