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A family of experiments on test-driven development

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Abstract

Context:

Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.

Objectives:

The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.

Method:

We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.

Results:

TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.

Conclusion:

Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.

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Notes

  1. For simplicity’s sake, we refer to quality and external quality interchangeably throughout the rest of the article. We acknowledge the limitations of this under the threats to validity.

  2. Note that in our experiments the programming language is confounded with other variables: IDE, testing tools, and other programming environment related variables (the use of Java implies the use of Java-related technologies, while the use of C++/C# implies the use of C++/C#-related technologies). We have grouped all confounded variables under the programming environment name.

  3. Due to space restrictions, we moved the references of the primary studies to the A.

  4. It was not feasible to compute any response ratio synthesizing the quality achieved with TDD with respect to a control approach.

  5. The outlier observed in [P35] may have been due to the small number of participants, and the larger variability of results expected in small sample sizes (Cumming 2013).

  6. Throughout the rest of the paper we refer to the treatment—terminology commonly used in experimental design and data analysis (Brown and Prescott 2014; Higgins et al. 2008; Juristo and Moreno 2001; Wohlin et al. 2012)—and the development approach (i.e., either ITL or TDD) interchangeably.

  7. https://github.com/GRISE-UPM/FiDiPro_ESEIL_TDD.

  8. Note that the implementation style influences the size of the programs. We do not claim that these are gold implementations with the optimal design and coding practices. In fact, there are several implementations of these katas in public GitHub repositories.

  9. Both measured with eclEmma: https://www.eclemma.org/

  10. Measured with muJava: https://cs.gmu.edu/~offutt/mujava/

  11. Note that the fact that participants do not have any ITL and TDD experience does not mean that they have no software testing experience. ITL and TDD have to do with knowledge of slicing and not with knowledge of testing. Therefore, participants with testing experience might conceivably have no experience with either ITL and/or TDD.

  12. https://www.eui.eu/documents/servicesadmin/deanofstudies/researchethics/guide-data-protection-research.pdf

  13. We analyzed the data with t-tests. Therefore, the mean difference (i.e., the slope of the line) provides useful information for evaluating experiment results.

  14. In our experiments, the IDEs and testing tools used with C++ and C# are different. In this study, however, we make the simplification of considering them as being a part of the same group of technologies merely for the purposes of comparison.

  15. A type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed.

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Acknowledgements

This research was developed with the support of project PGC2018-097265-B-I00, funded by: FEDER/Spanish Ministry of Science and Innovation—Research State Agency. We would like to thank the participants in the ESEIL experiments: this research would not have been possible without your help. We would also like to thank the anonymous reviewers for their valuable comments during the review of the manuscript.

Author information

Authors and Affiliations

  1. University of Oulu, Oulu, Finland

    Adrian Santos, Burak Turhan, Itir Karac & Markku Oivo

  2. Universidad Politécnica de Madrid, Madrid, Spain

    Sira Vegas, Oscar Dieste & Natalia Juristo

  3. ETAPA, Cuenca, Ecuador

    Fernando Uyaguari & Carolina Palomeque

  4. Istanbul Technical University, Istanbul, Turkey

    Ayşe Tosun

  5. Blekinge Institute of Technology, Karlskrona, Sweden

    Davide Fucci

  6. University of Basilicata, Potenza, Italy

    Giuseppe Scanniello

  7. University of Bari, Bari, Italy

    Simone Romano

  8. University of Passau, Passau, Germany

    Marco Kuhrmann

  9. University of Novi Sad, Novi Sad, Serbia

    Vladimir Mandić & Robert Ramač

  10. University of Tartu, Tartu, Estonia

    Dietmar Pfahl

  11. Ericsson, Jorvas, Finland

    Christian Engblom & Jarno Kyykka

  12. Testlio, Tartu, Estonia

    Kerli Rungi

  13. Paf, Helsinki, Finland

    Jaroslav Spisak

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Correspondence to Sira Vegas.

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Communicated by: Jeff Offutt

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Appendix: : Primary Studies

Appendix: : Primary Studies

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Santos, A., Vegas, S., Dieste, O. et al. A family of experiments on test-driven development. Empir Software Eng 26, 42 (2021). https://doi.org/10.1007/s10664-020-09895-8

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