On the search for industry-relevant regression testing research

Research output: Contribution to journalArticle

Abstract

Regression testing is a means to assure that a change in the software, or its execution environment, does not introduce new defects. It involves the expensive undertaking of rerunning test cases. Several techniques have been proposed to reduce the number of test cases to execute in regression testing, however, there is no research on how to assess industrial relevance and applicability of such techniques. We conducted a systematic literature review with the following two goals: firstly, to enable researchers to design and present regression testing research with a focus on industrial relevance and applicability and secondly, to facilitate the industrial adoption of such research by addressing the attributes of concern from the practitioners’ perspective. Using a reference-based search approach, we identified 1068 papers on regression testing. We then reduced the scope to only include papers with explicit discussions about relevance and applicability (i.e. mainly studies involving industrial stakeholders). Uniquely in this literature review, practitioners were consulted at several steps to increase the likelihood of achieving our aim of identifying factors important for relevance and applicability. We have summarised the results of these consultations and an analysis of the literature in three taxonomies, which capture aspects of industrial-relevance regarding the regression testing techniques. Based on these taxonomies, we mapped 38 papers reporting the evaluation of 26 regression testing techniques in industrial settings.

Details

Authors
Organisations
External organisations
  • University of Leicester
  • Halmstad University
  • Blekinge Institute of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Software Engineering

Keywords

  • Regression testing, Industrial relevance, Systematic literature review, Taxonomy, Recommendations
Original languageEnglish
Pages (from-to)2020-2055
JournalEmpirical Software Engineering
Volume24
Issue number4
Early online date2019 Feb 12
Publication statusPublished - 2019
Publication categoryResearch
Peer-reviewedYes