SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

  • Jianmei Guo*
  • , Jia Hui Liang
  • , Kai Shi
  • , Dingyu Yang
  • , Jingsong Zhang
  • , Krzysztof Czarnecki
  • , Vijay Ganesh
  • , Huiqun Yu
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all predefined constraints and balance multiple often competing objectives. To address this challenge, we propose a hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving. We evaluated the proposed algorithm on five large, constrained, real-world SPLs. Compared to the state-of-the-art, our approach significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance. Furthermore, we investigate the performance influence of the SMT solving on two evolutionary operators of the IBEA.

Original languageEnglish
Pages (from-to)1447-1466
Number of pages20
JournalSoftware and Systems Modeling
Volume18
Issue number2
DOIs
StatePublished - 4 Apr 2019
Externally publishedYes

Keywords

  • Constraint solving
  • Feature models
  • Multi-objective evolutionary algorithms
  • Search-based software engineering
  • Software product lines

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