Metamorphic testing and test automation

Abstract

This issue contains two papers. The first paper focuses on metamorphic testing and the second one focuses on test automation.Thefirst paper, ‘ Metamorphic relation prioritization for effective regression testing ’ by Madhusudan Srinivasan and Upulee Kanewala, concerns metamorphic testing. Metamorphic testing (MT) is an approach devised to support the testing of software that is untestable in the sense that it is not feasible to determine, in advance, the expected output for a given test input. The basic idea behind MT is that it is sometimes possible to provide a property (metamorphic relation) over multiple test runs that use inputs that are related in some way. A classic example is that we may not know what the cosine of x should be for some arbitrary x but we do know that cos( x ) should be the same as cos( (cid:1) x ). Previous work has proposed the use of multiple metamorphic relations (MRs), but the authors explore how one might prioritize (order) such MRs. Prioritization is based on information regarding a previous version of the software under test. The authors propose two approaches: prioritize on coverage or on fault detection. Optimization is achieved using a greedy algorithm that is sometimes called Additional Greedy. (Recommended by Dan Hao). The second paper, ‘ Improving test automation maturity: A multivocal literature review ’ by Yuqing Wang, Mika V. Mäntylä, Zihao Liu, Jouni Markkula and Päivi Raulamo-jurvanen, presents a multivocal literature review to survey and synthesize the guidelines given in the literature for improving test automation maturity. The authors select and review 81 primary studies (26 academic literature sources and 55 grey literature sources). From these primary studies, the authors extract 26 test automation best practices along with advice on how to conduct these best practices in forms of implementation/improvement approaches, actions, technical techniques, concepts and experience-based opinions. In particular, the literature review results contribute test automation best practices to suggest steps for improving test automation maturity, narrow the gap between practice and research in terms of the industry ’ s need to improve test automation maturity, provide a centralized knowledge base of existing guidelines for test automation maturity improvement and identify related research challenge and opportunities.