the folkloric advice [...], replace them with [...] empirical methods – Bertrand Meyer [Meyer, 2013] “ ” [Meyer, 2013] B. Meyer, H. Gall, M. Harman, and G. Succi, “Empirical Answers to Fundamental Software Engineering Problems (Panel),” in Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, ser. ESEC/FSE 2013. New York, USA: ACM, 2013, pp. 14–18. Picture: https://commons.wikimedia.org/wiki/File:Bertrand_Meyer_recent.jpg
founded on empirical process control theory [...]. Three pillars [...]: transparency, inspection, and adaptation. – The Scrum Guide [Schwaber, 2017] “ ” [Schwaber, 2017] K. Schwaber, J. Sutherland, “The Scrum Guide - The Deﬁnitive Guide to Scrum,” 2017, [online] http://scrumguides.org/docs/scrumguide/v2017/2017-Scrum-Guide-US.pdf Picture: https://www.scrum.org/resources/2017-scrum-guide-update-ken-schwaber-and-jeﬀ-sutherland
R., & Torkar, R. “The Unfulﬁlled Potential of Data-Driven Decision Making in Agile Software Development”, 20th International Conference on Agile Software Development (XP), 2019 (preprint), https://arxiv.org/abs/1904.03948
software practitioners ▪ How is data used in the company for making decisions? [Svensson, 2019] Svensson, R.B., Feldt, R., & Torkar, R. “The Unfulﬁlled Potential of Data-Driven Decision Making in Agile Software Development”, 20th International Conference on Agile Software Development (XP), 2019 (preprint), https://arxiv.org/abs/1904.03948
al., 2016] ▪ Project data is continuously produced by development teams ▪ Holds insights into team processes code code analyses Project Data documentation Primary purpose: Communication Opportunity: Process Insights ... [Kalliamvakou et al., 2016] Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., Damian, D. “An in-depth study of the promises and perils of mining GitHub”. Empirical Software Engineering, 21(5), pp. 2035–2071. 2016. https://doi.org/10.1007/s10664-015-9393-5
data is already created by Agile teams during regular development activities. ▪ It holds extensive information on how team members work and collaborate. ▪ Teams can use analyses of this data to inform and track their process improvement steps.
techniques [Dyer et al., 2013] ▪ However, mostly focus on large amounts of code □ “What do README ﬁles look like?” [Prana et al., 2018] □ “most widely used open source license?” [Dyer et al., 2013] ▪ Little research: Few repositories, intricate knowledge of creators / users [Prana et al., 2018] Prana, G. A. A., Treude, C., Thung, F., Atapattu, T., & Lo, D. “Categorizing the Content of GitHub README Files”. Empirical Software Engineering. 2018. https://doi.org/10.1007/s10664-018-9660-3 [Dyer et al., 2013] Dyer, R., Nguyen, H. A., Rajan, H., & Nguyen, T. N. “Boa: A language and infrastructure for analyzing ultra-large-scale software repositories”. In Proceedings - International Conference on Software Engineering. pp. 422–431. 2013. IEEE.
provided actionable insights □ into team processes [1,2] □ for exercise improvement  □ for improving teaching eﬀorts [4,5] ▪ Measurements from course experience and from literature  Matthies, C., Kowark, T., Richly, K., Uﬂacker, M., & Plattner, H. “How Surveys, Tutors, and Software Help to Assess Scrum Adoption”. In Proceedings of the 38th International Conference on Software Engineering Companion - ICSE ’16. pp. 313–322 2016  Matthies, C., Kowark, T., Uﬂacker, M., & Plattner, H. “Agile Metrics for a University Software Engineering Course”. In 2016 IEEE Frontiers in Education Conference (FIE). pp. 1–5. 2016.  Matthies, C., Treﬀer, A., & Uﬂacker, M. “Prof. CI: Employing Continuous Integration Services and GitHub Workﬂows to Teach Test-Driven Development”. In 2017 IEEE Frontiers in Education Conference (FIE). pp. 1–8. 2017  Matthies, C. “Scrum2kanban: Integrating Kanban and Scrum in a University Software Engineering Capstone Course”. In Proceedings of the 2nd International Workshop on Software Engineering Education for Millennials - SEEM ’18. pp. 48–55. 2018  Matthies, C., Teusner, R., & Hesse, G. “Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Eﬀorts”. In 2018 IEEE Frontiers in Education Conference (FIE). pp. 1–9. 2018
transferable to industry □ Experienced professionals working full-time □ Custom development processes ▪ Study challenges of improving processes in industry □ How are Retrospectives implemented in industry? □ What are the outcomes of Retrospectives? □ Can / are action items tracked?
interviews in companies (Wikimedia, Signavio, Nokia HERE, SAP Teams) □ Project data usage: None to Jira with custom plugins □ Little usage of data for process improvement (except Kanban cycle time) □ No mentions of using data for tracking retro issues: “regression tests for processes” ▪ Interest in application of project data analysis for everything (also for management) ▪ Retrospectives not as mature as assumed
Is project data being used or considered useful? ▪ Collect and organize the Retrospective outcomes in industry □ Action items which are directly related to data vs. those that are not, e.g. interpersonal issues. ▪ Form further hypotheses on how teams can be supported with tools for process improvement
by Shocho from the Noun Project (CC BY 3.0 US) ▪ Mortar Board by Mike Chum from the Noun Project (CC BY 3.0 US) ▪ Target by Arthur Shlain from the Noun Project (CC BY 3.0 US) ▪ Paper By LUTFI GANI AL ACHMAD, ID the Noun Project (CC BY 3.0 US)