Experiments are a commonly used method of research in software engineering (SE). Researchers report their experiments following detailed guidelines. However, researchers do not, in the field of test-driven development (TDD) at least, specify how they operationalized the response variables and, particularly, the measurement process. This article has three aims: (i) identify the response variable operationalization components in TDD experiments that study external quality; (ii) study their influence on the experimental results; (iii) determine if the experiment reports describe the measurement process components that have an impact on the results. We used two-part sequential mixed methods research. The first part of the research adopts a quantitative approach applying a statistical analysis of the impact of the operationalization components on the experimental results. The second part follows with a qualitative approach applying a systematic mapping study (SMS). The test suites, intervention types and measurers have an influence on the measurements and results of the statistical analysis of TDD experiments in SE. The test suites have a major impact on both the measurements and the results of the experiments. The intervention type has less impact on the results than on the measurements. While the measurers have an impact on the measurements, this is not transferred to the experimental results. On the other hand, the results of our SMS confirm that TDD experiments do not usually report either the test suites, the test case generation method, or the details of how external quality was measured. A measurement protocol should be used to ensure that the measurements made by different measurers are similar. It is necessary to report the test cases, the experimental task and the intervention type in order to be able to reproduce the measurements and statistical analyses, as well as to replicate experiments and build dependable families of experiments.

Abstract In the context of requirements specification in Global Software Development, aspects such as differences in culture, language and schedule affect software development teams; however, we do not know the effect of these issues. Compare the native language requirements with the foreign language requirements concerning external quality. We conducted a controlled experiment of one‐factor two treatments within‐subjects with 17 experimental subjects. Wilcoxon test indicates that there is evidence to reject the null hypothesis ( p ‐value = 0.008); there is a statistically significant difference. The external quality value obtained with native language requirements is superior to the external quality produced with the foreign language. The effect size equals an absolute value of 0.45, which corresponds to a medium effect. The language used in the requirements specification influences the external quality; using the native language in the requirements specification significantly increases the external quality. The result obtained in this research should be considered to evaluate the roles and English language skills of GSD team members and their effect on external software quality. We also suggest considering the English language skills of the experimental subjects in the experimental laboratories since language could influence the results of the experiments.

Abstract Software engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments’ weaknesses and increasing the reliability of the findings. Illustrating the benefits of replication to increase the reliability of the findings and uncover moderator variables. We replicate an experiment on test-driven development (TDD) and address some of its threats to validity and those of a previous replication. We compare the replications’ results and hypothesize on plausible moderators impacting results. Differences across TDD replications’ results might be due to the operationalization of the response variables, the allocation of subjects to treatments, the allowance to work outside the laboratory, the provision of stubs, or the task. Replications allow examining the robustness of the findings, hypothesizing on plausible moderators influencing results, and strengthening the evidence obtained.

This extended abstract summarizes an article, which has been published in the Empirical Software Engineering Journal and was selected for the Journal-First presentations at the International Conference on Software and System Process (ICSSP 2018).

Context: Test-driven development (TDD) is an agile practice claimed to improve the quality of a software product, as well as the productivity of its developers. A previous study (i.e., baseline experiment) at the University of Oulu (Finland) compared TDD to a test-last development (TLD) approach through a randomized controlled trial. The results failed to support the claims. Goal: We want to validate the original study results by replicating it at the University of Basilicata (Italy), using a different design. Method: We replicated the baseline experiment, using a crossover design, with 21 graduate students. We kept the settings and context as close as possible to the baseline experiment. In order to limit researchers bias, we involved two other sites (UPM, Spain, and Brunel, UK) to conduct blind analysis of the data. Results: The Kruskal-Wallis tests did not show any significant difference between TDD and TLD in terms of testing effort (p-value = .27), external code quality (p-value = .82), and developers' productivity (p-value = .83). Nevertheless, our data revealed a difference based on the order in which TDD and TLD were applied, though no carry over effect. Conclusions: We verify the baseline study results, yet our results raises concerns regarding the selection of experimental objects, particularly with respect to their interaction with the order in which of treatments are applied.