Evolutionary search-based test generation for software product line feature models

Faezeh Ensan, Ebrahim Bagheri, Dragan Gašević

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

59 Citations (Scopus)


Product line-based software engineering is a paradigm that models the commonalities and variabilities of different applications of a given domain of interest within a unique framework and enhances rapid and low cost development of new applications based on reuse engineering principles. Despite the numerous advantages of software product lines, it is quite challenging to comprehensively test them. This is due to the fact that a product line can potentially represent many different applications; therefore, testing a single product line requires the test of its various applications. Theoretically, a product line with n software features can be a source for the development of 2 n application. This requires the test of 2 n applications if a brute-force comprehensive testing strategy is adopted. In this paper, we propose an evolutionary testing approach based on Genetic Algorithms to explore the configuration space of a software product line feature model in order to automatically generate test suites. We will show through the use of several publicly-available product line feature models that the proposed approach is able to generate test suites of O(n) size complexity as opposed to O(2 n ) while at the same time form a suitable tradeoff balance between error coverage and feature coverage in its generated test suites.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 24th International Conference, CAiSE 2012, Proceedings
Number of pages16
Publication statusPublished - 29 Oct 2012
Externally publishedYes
EventInternational Conference on Advanced Information Systems Engineering 2012 - Gdansk, Poland
Duration: 25 Jun 201229 Jun 2012
Conference number: 24th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7328 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Advanced Information Systems Engineering 2012
Abbreviated titleCAiSE 2012


  • Evolutionary testing
  • Feature models
  • Software product lines

Cite this