mousavi-emse-2019.pdf (11.5 MB)
Download file

On the Search for Industry-Relevant Regression Testing Research

Download (11.5 MB)
journal contribution
posted on 14.02.2019, 12:07 by N Bin Ali, E Engstrom, M Taromirad, M Mousavi, NM Minhas, D Helgesson, S Kunze, M Varshosaz
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.

Funding

The work of Mohammad Reza Mousavi and Masoumeh Taromirad has been partially supported by (Vetenskapsrådet) award number: 621-2014-5057 (Effective Model-Based Testing of Concurrent Systems) and by the Swedish Knowledge Foundation (Stiftelsen for Kunskaps- och Kompetensutveckling) in the context of the AUTO-CAAS HÖG project (number: 20140312).

History

Citation

Empirical Software Engineering, pp 1-36

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

AM (Accepted Manuscript)

Published in

Empirical Software Engineering

Publisher

Springer Verlag

issn

1382-3256

eissn

1573-7616

Acceptance date

21/11/2018

Copyright date

2019

Available date

14/02/2019

Publisher version

https://link.springer.com/article/10.1007/s10664-018-9670-1

Language

en

Usage metrics

Categories

Keywords

Licence

Exports