Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/12199
Title: SIP: Optimal Product Selection from Feature Models using Many-Objective Evolutionary Optimisation
Authors: Hierons, RM
Li, M
Liu, X
Segura, S
Zheng, W
Keywords: Software and its emgineering;Software product lines;Mathematics of computing;Optimization with randomized search heuristics;Product selection
Issue Date: 2016
Publisher: Association for Computing Machinery (ACM)
Citation: ACM Transactions on Software Engineering and Methodology, 25(2): Article No.17, (2016)
Abstract: A feature model specifies the sets of features that define valid products in a software product line. Recent work has considered the problem of choosing optimal products from a feature model based on a set of user preferences, with this being represented as a many-objective optimisation problem. This problem has been found to be difficult for a purely search-based approach, leading to classical many-objective optimisation algorithms being enhanced by either adding in a valid product as a seed or by introducing additional mutation and replacement operators that use a SAT solver. In this paper we instead enhance the search in two ways: by providing a novel representation and also by optimising first on the number of constraints that hold and only then on the other objectives. In the evaluation we also used feature models with realistic attributes, in contrast to previous work that used randomly generated attribute values. The results of experiments were promising, with the proposed (SIP) method returning valid products with six published feature models and a randomly generated feature model with 10,000 features. For the model with 10,000 features the search took only a few minutes.
URI: http://bura.brunel.ac.uk/handle/2438/12199
DOI: http://doi.dx.org/10.1145/2897760
ISSN: 1557-7392
Appears in Collections:Dept of Computer Science Research Papers

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