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Different Debt: An Addition to the Technical Debt Dataset and a Demonstration Using Developer Personality (2403.01157v1)

Published 2 Mar 2024 in cs.SE

Abstract: Background: The "Technical Debt Dataset" (TDD) is a comprehensive dataset on technical debt (TD) in the main branches of more than 30 Java projects. However, some TD items produced by SonarQube are not included for many commits, for instance because the commits failed to compile. This has limited previous studies using the dataset. Aims and Method: In this paper, we provide an addition to the dataset that includes an analysis of 278,320 commits of all branches in a superset of 37 projects using Teamscale. We then demonstrate the utility of the dataset by exploring the relationship between developer personality by replicating a prior study. Results: The new dataset allows us to use a larger sample than prior work could, and we analyze the personality of 111 developers and 5,497 of their commits. The relationships we find between developer personality and the introduction and removal of TD differ from those found in prior work. Conclusions: We offer a dataset that may enable future studies into the topic of TD and we provide additional insights on how developer personality relates to TD.

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References (31)
  1. An exploratory study on the influence of developers in technical debt. In Proceedings of the 2018 International Conference on Technical Debt (ACM Conferences), Robert L. Nord (Ed.). ACM, New York, NY, 1–10. https://doi.org/10.1145/3194164.3194165
  2. Identification and management of technical debt: A systematic mapping study. Information and Software Technology 70 (2016), 100–121. https://doi.org/10.1016/j.infsof.2015.10.008
  3. The NPI-16 as a short measure of narcissism. Journal of Research in Personality 40, 4 (2006), 440–450. https://doi.org/10.1016/j.jrp.2005.03.002
  4. Managing Technical Debt in Software Engineering (Dagstuhl Seminar 16162). Dagstuhl Reports 6, 4 (2016), 110–138.
  5. The influence of Technical Debt on software developer morale. Journal of Systems and Software 167 (2020), 110586. https://doi.org/10.1016/j.jss.2020.110586
  6. The Pricey Bill of Technical Debt: When and by Whom will it be Paid?. In ICSME 2017, IEEE International Conference on Software Maintenance and Evolution (Ed.). IEEE, Piscataway, NJ, 13–23. https://doi.org/10.1109/ICSME.2017.42
  7. A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem. Information and Software Technology 114 (2019), 1–20. https://doi.org/10.1016/j.infsof.2019.05.012
  8. Zadia Codabux and Christopher Dutchyn. 2020. Profiling Developers Through the Lens of Technical Debt. In Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, New York, NY, USA, 1–6. https://doi.org/10.1145/3382494.3422172
  9. Ward Cunningham. 1992. The WyCash Portfolio Mangement System. In Addendum to the Proceedings of OOPSLA 1992. 29–30.
  10. Looking for Peace of Mind? Manage Your (Technical) Debt: An Exploratory Field Study. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 384–393. https://doi.org/10.1109/ESEM.2017.53
  11. A very brief measure of the Big-Five personality domains. Journal of Research in Personality 37, 6 (2003), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1
  12. Lorenz Graf-Vlachy. 2023. The Risk-Taking Software Engineer: A Framed Portrait. In 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). IEEE, 25–30. https://doi.org/10.1109/ICSE-NIER58687.2023.00011
  13. Lorenz Graf-Vlachy and Stefan Wagner. 2023. The Type to Take Out a Loan? A Study of Developer Personality and Technical Debt. In 2023 ACM/IEEE International Conference on Technical Debt (TechDebt). IEEE, 27–36. https://doi.org/10.1109/TechDebt59074.2023.00010
  14. Teamscale: Tackle Technical Debt and Control the Quality of Your Software. In 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). 55–56. https://doi.org/10.1109/TechDebt.2019.00016
  15. An Assessment of Chronic Regulatory Focus Measures. Journal of Marketing Research 47, 5 (2010), 967–982. https://doi.org/10.1509/jmkr.47.5.967
  16. Teamscale: software quality control in real-time. In Companion Proceedings of the 36th International Conference on Software Engineering, Pankaj Jalote, Lionel Briand, and André van der Hoek (Eds.). ACM, New York, NY, USA, 592–595. https://doi.org/10.1145/2591062.2591068
  17. On the Lack of Consensus Among Technical Debt Detection Tools. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 121–130. https://doi.org/10.1109/ICSE-SEIP52600.2021.00021
  18. Are SonarQube Rules Inducing Bugs?. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 501–511. https://doi.org/10.1109/SANER48275.2020.9054821
  19. Towards surgically-precise technical debt estimation: early results and research roadmap. In Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation - MaLTeSQuE 2019, Francesca Arcelli Fontana, Bartosz Walter, Apostolos Ampatzoglou, Fabio Palomba, Gilles Perrouin, Mathieu Acher, Maxime Cordy, and Xavier Devroey (Eds.). ACM Press, New York, New York, USA, 37–42. https://doi.org/10.1145/3340482.3342747
  20. On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. In 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE, 98–107. https://doi.org/10.1109/TechDebt.2019.00028
  21. The Technical Debt Dataset. In Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering, Leandro Minku, Foutse Khomh, and Jean Petrić (Eds.). ACM, New York, NY, USA, 2–11. https://doi.org/10.1145/3345629.3345630
  22. Rainer Niedermayr. 2016. Why we don’t use the Software Maintainability Index. https://www.cqse.eu/en/news/blog/maintainability-index/
  23. Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk. PeerJ. Computer science 5 (2019), e187. https://doi.org/10.7717/peerj-cs.187
  24. Ken Power. 2013. Understanding the impact of technical debt on the capacity and velocity of teams and organizations: Viewing team and organization capacity as a portfolio of real options. In 2013 4th International Workshop on Managing Technical Debt (MTD 2013), Philippe Kruchten (Ed.). IEEE, Piscataway, NJ, 28–31. https://doi.org/10.1109/MTD.2013.6608675
  25. Prevalence, common causes and effects of technical debt: Results from a family of surveys with the IT industry. Journal of Systems and Software 184 (2022), 111114. https://doi.org/10.1016/j.jss.2021.111114
  26. A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners. Information and Software Technology 102 (2018), 117–145. https://doi.org/10.1016/j.infsof.2018.05.010
  27. Klaus Schmid. 2013. On the limits of the technical debt metaphor some guidance on going beyond. In 2013 4th International Workshop on Managing Technical Debt (MTD 2013), Philippe Kruchten (Ed.). IEEE, Piscataway, NJ, 63–66. https://doi.org/10.1109/MTD.2013.6608681
  28. Frank L. Schmidt and John E. Hunter. 1996. Measurement error in psychological research: Lessons from 26 research scenarios. Psychological Methods 1, 2 (1996), 199–223. https://doi.org/10.1037/1082-989X.1.2.199
  29. An exploration of technical debt. Journal of Systems and Software 86, 6 (2013), 1498–1516. https://doi.org/10.1016/j.jss.2012.12.052
  30. Challenges in Survey Research. In Contemporary Empirical Methods in Software Engineering, Michael Felderer and Guilherme Horta Travassos (Eds.). Springer International Publishing, Cham, 93–125. https://doi.org/10.1007/978-3-030-32489-6{_}4
  31. Jeffrey M. Wooldridge. 2010. Econometric analysis of cross section and panel data (2nd ed. ed.). MIT Press, Cambridge.

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