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Feedback in Scrum: Data-Informed Retrospectives

Feedback in Scrum: Data-Informed Retrospectives

Slides of the talk given at the Doctoral Symposium of ICSE 2019.
https://2019.icse-conferences.org/track/icse-2019-Doctoral-Symposium#program

Christoph Matthies

May 28, 2019
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  1. Hasso Plattner Institute
    University of Potsdam, Germany
    [email protected]
    @chrisma0
    Feedback in Scrum:
    Data-Informed Retrospectives
    Christoph Matthies
    Doctoral Symp., Canada, May ’19

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  2. Motivation
    2
    Software Engineering in General
    Software engineering must shed 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

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  3. Motivation
    3
    The Role of Data in Scrum
    Scrum is 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

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  4. Main Research Topic
    4
    Likely PhD Thesis Topic
    Supporting agile teams
    in their process adaptation efforts
    using transparency
    and inspection of
    their own project data

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  5. Related Work
    5
    [Svensson, 2019]
    [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

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  6. Unfulfilled Potential of DDDM
    6
    [Svensson, 2019]
    ■ Survey of 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

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  7. Software Project Data
    7
    Mining Repositories of Teams [Kalliamvakou et 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

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  8. Agile Process Improvement
    8
    The Retrospective Meeting
    ■ Scrum’s dedicated process improvement meeting
    ■ Feedback on the product as well as the process

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  9. The Retrospective
    9
    Tracking Retrospective Action Items
    Did we improve
    what we planned?
    commits,
    reviews
    test runs
    tickets
    static
    analysis
    Retrospective
    Meeting
    Project Data
    Evidence of last
    iteration’s work

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  10. Current Research Hypothesis
    10
    Towards Data-Informed Process Improvement
    ■ Development 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.

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  11. Related Work
    11
    Mining Software Repositories
    ■ Draw from MSR 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.

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  12. Contributions So Far
    12
    ■ Development data of student teams provided actionable insights
    □ into team processes [1,2]
    □ for exercise improvement [3]
    □ for improving teaching efforts [4,5]
    ■ Measurements from course experience and from literature
    [1] 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
    [2] 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.
    [3] 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
    [4] 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
    [5] 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

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  13. Next Steps
    13
    Application in Industry
    ■ Learnings not directly 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?

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  14. Current Industry Study
    14
    Interviews with Agile Facilitators
    ■ Initial 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

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  15. Next Steps in Industry
    15
    Interviews with Agile Facilitators
    ■ 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

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  16. Image Credits
    17
    In order of appearance
    ■ retrospective meeting 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)

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