MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines
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|Title:||MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines||Authors:||Saber, Takfarinas; Brevet, David; Botterweck, Goetz; Ventresque, Anthony||Permanent link:||http://hdl.handle.net/10197/11844||Date:||9-Apr-2020||Online since:||2021-01-15T14:54:38Z||Abstract:||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).||Funding Details:||Science Foundation Ireland||Type of material:||Book Chapter||Publisher:||Springer International Publishing||Start page:||164||End page:||179||Series/Report no.:||Lecture Notes in Computer Science; 12102||Copyright (published version):||2020 Springer Nature||Keywords:||Software product line; Feature selection; Multi-objective optimisation; Evolutionary algorithm; Mixed-integer linear programming||DOI:||10.1007/978-3-030-43680-3_11||Language:||en||Status of Item:||Peer reviewed||Is part of:||Paquete L., Zarges C. (eds.)., Evolutionary Computation in Combinatorial Optimization||ISBN:||9783030436797||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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