SIP: optimal product selection from feature models using many-objective evolutionary optimization

Robert M. Hierons, Miqing Li, Xiaohui Liu, Sergio Segura, Wei Zheng

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)
74 Downloads (Pure)

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 optimization problem. This problem has been found to be difficult for a purely search-based approach, leading to classical many-objective optimization algorithms being enhanced either by adding in a valid product as a seed or by introducing additional mutation and replacement operators that use an SAT solver. In this article, we instead enhance the search in two ways: by providing a novel representation and by optimizing 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.
Original languageEnglish
Article number17
Number of pages39
JournalACM Transactions on Software Engineering and Methodology
Volume25
Issue number2
Early online date30 Apr 2016
DOIs
Publication statusPublished - May 2016

Keywords

  • Software product lines
  • Optimization with randomized search heuristics
  • Product selection

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