Päivi Raulamo-Jurvanen

Yuqing Wang, Mika V. Mäntylä, Zihao Liu et al.

Mature test automation is key for achieving software quality at speed. In this paper, we present a multivocal literature review with the objective to survey and synthesize the guidelines given in the literature for improving test automation maturity. We selected and reviewed 81 primary studies, consisting of 26 academic literature and 55 grey literature sources. From primary studies, we extracted 26 test automation best practices (e.g., Define an effective test automation strategy, Set up good test environments, Develop high-quality test scripts) and collected many pieces of advice (e.g., in forms of implementation/improvement approaches, technical techniques, concepts, experience-based heuristics) on how to conduct these best practices. We made main observations: (1) There are only 6 best practices whose positive effect on maturity improvement have been evaluated by academic studies using formal empirical methods; (2) Several technical related best practices in this MLR were not presented in test maturity models; (3) Some best practices can be linked to success factors and maturity impediments proposed by other scholars; (4) Most pieces of advice on how to conduct proposed best practices were identified from experience studies and their effectiveness need to be further evaluated with cross-site empirical evidence using formal empirical methods; (5) In the literature, some advice on how to conduct certain best practices are conflicting, and some advice on how to conduct certain best practices still need further qualitative analysis.

Yuqing Wang, Mika Mäntylä, Sigrid Eldh et al.

Test automation is important in software industry but self-assessment instruments for assessing its maturity are not sufficient. The two objectives of this study are to synthesize what an organization should focus to assess its test automation; develop a self-assessment instrument (a survey) for assessing test automation maturity and scientifically evaluate it. We carried out the study in four stages. First, a literature review of 25 sources was conducted. Second, the initial instrument was developed. Third, seven experts from five companies evaluated the initial instrument. Content Validity Index and Cognitive Interview methods were used. Fourth, we revised the developed instrument. Our contributions are as follows: (a) we collected practices mapped into 15 KAs that indicate where an organization should focus to assess its test automation; (b) we developed and evaluated a self-assessment instrument for assessing test automation maturity; (c) we discuss important topics such as response bias that threatens self-assessment instruments. Our results help companies and researchers to understand and improve test automation practices and processes.

Miikka Kuutila, Mika Mäntylä, Päivi Raulamo-Jurvanen

Context: Selenium is claimed to be the most popular software test automation tool. Past academic works have mainly neglected testing tools in favor of more methodological topics. Objective: We investigated the performance of web-testing tools, to provide empirical evidence supporting choices in software test tool selection and configuration. Method: We used 4*5 factorial design to study 20 different configurations for testing a web-store. We studied 5 programming language bindings (C#, Java, Python, and Ruby for Selenium, while Watir supports Ruby only) and 4 browsers (Google Chrome, Internet Explorer, Mozilla Firefox and Opera). Performance was measured with execution time, memory usage, length of the test scripts and stability of the tests. Results: Considering all measures the best configuration was Selenium with Python language binding for Chrome. Selenium with Python bindings was the best option for all browsers. The effect size of the difference between the slowest and fastest configuration was very high (Cohens d=41.5, 91% increase in execution time). Overall Internet Explorer was the fastest browser while having the worst results in the stability. Conclusions: We recommend benchmarking tools before adopting them. Weighting of factors, e.g. how much test stability is one willing to sacrifice for faster performance, affects the decision.