Agile Software Development Practices: Perceptions & Project Data

Talk on agile software development practices and their relationship to team members perceptions, held at the 2020 Software Engineering (SE) conference, organized by the Gesellschaft für Informatik (GI), 24-28 Feb 2020 in Innsbruck, Austria. Conference website: https://se20.ocg.at/

Paper :
C. Matthies, J. Huegle, T. Dürschmid, and R. Teusner, “Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices,” in Software Engineering 2020, M. Felderer, W. Hasselbring, R. Rabiser, and R. Jung, Eds., Bonn: Gesellschaft für Informatik e.V., 2020, pp. 73–74. doi: 10.18420/SE2020_20 (CC BY-SA 4.0)
[Online] available: https://dl.gi.de/handle/20.500.12116/31697

Transcript

1. Hasso Plattner Institute, University of Potsdam, Germany
   *Carnegie Mellon University, Pittsburgh, USA
   christoph.matthies@hpi.de
   @chrisma0
   Agile Software Development Practices:
   Perceptions & Project Data
   Christoph Matthies, Johannes Hügle,
   Tobias Dürschmid*, Ralf Teusner
   February ’20
   Innsbruck
   

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2. Background
   2
   An undergraduate software engineering capstone course
   “
   methods, concepts, and technologies that
   help successfully deliver large software
   products developed by multiple teams. [1]
   [1] https://hpi.de/plattner/teaching/archive/winter-term-201819/softwaretechnik-ii.html
   ”
   

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3. Motivation: Agile SE Teams
   3
   Teamwork in collaborating (student) groups
   

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4. 4
   Motivation: Agile SE Teams
   Teamwork in collaborating (student) groups
   

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5. 5
   Relationship between
   Process Perceptions and Project Data
   Motivation: Perceptions & Data
   Two worlds, each relevant
   

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6. Perceptions vs. Project Data
   Main research questions
   ■ Q1: What are perceptions of agile practice usage
   in student project teams?
   ■ Q2: Which practices are perceived to be
   most related to agile values?
   ■ Q3: What is the relationship of
   perceptions and software project data?
   6
   Regarding agile practices
   

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7. Perceptions vs. Project Data
   ■ SE lecture with agile (collaboration) process novices
   ■ Subset of eight agile best practices under study
   ■ Repeatedly collect perceptions of practice use
   ■ 42 students
   ■ Surveys after each of the four Sprints
   ■ Define data measurements
   reflecting Agile practice usage
   7
   Methods
   

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8. Overall Survey Results
   8
   Q1: Perceptions regarding practices, answers over all sprints
   

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9. Overall Survey Results
   9
   Q1: Perceptions regarding practices, answers over all sprints
   

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10. Overall Survey Results
    10
    Q2: Correlations to perceptions of agile value implementation
    

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11. Overall Survey Results
    11
    Practices most related to “Agile Mindset”:
    ■ Practicing Collective Code Ownership
    (Q2, τ=0.15, p < .05)
    ■ Not working near the deadline
    (Q5, τ=−0.21, p < .01)
    ■ Following “check in early,
    check in often” principle
    (Q6, τ= 0.24, p < .01)
    Q2: Correlations to perceptions of agile value implementation
    

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12. Evidence of Agile Practice Usage
    ■ Dev. practices “inscribed into software artifacts” [deSouza, 2005]
    ■ Analysis of teams’ GitHub project data
    ■ Define measures of agile practice usage
    □ Based on previous related literature where available
    □ “Analytics cold-start” problem [deSouza, 2005]
    □ Intuitively traceable to underlying data
    12
    Based on project data evidence
    [deSouza et al., 2005] de Souza, C., Froehlich, J., & Dourish, P, “Seeking the Source: Software Source Code as a
    Social and Technical Artifact”. In Proceedings of the 2005 international ACM SIGGROUP conference on
    Supporting group work - GROUP ’05, p. 197, 2005.
    

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13. Extract of Employed Measures
    ■ Code reviews
    □ Amount of Pull Request comments by a developer in a Sprint
    ■ Test-driven Development
    □ Ruby on Rails conventions separate test from application code
    □ Ratio of line changes in test and application code
    ■ Last-Minute Commits
    □ Percentage of commits by developer
    within 12 hours of sprint review meeting
    ■ ...
    13
    Agile practice measures based on project data
    

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14. Code Reviews in PRs
    ■ Expectations
    □ Many developers with few comments
    □ Barrier for leaving comments
    ■ Some devs very motivated: “Hero reviewers” cf. [Mockus et al., 2002]
    14
    Amount of code review comments
    Amount of comments per reviewer
    Frequency
    [Mockus et al., 2002] Mockus, Audris, Roy T. Fielding, and James D. Herbsleb, "Two case studies of open source software development:
    Apache and Mozilla," ACM Transactions on Software Engineering and Methodology (TOSEM) 11, no. 3, pp. 309-346, 2002.
    

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15. Test-Driven Development
    ■ Test Statements per Solution Statement [Buffardi et al., 2012]
    ■ Expectation: Low ratios of test to app code changes
    ■ 10 test LOC changes / 100 app LOC changes = 0,1
    15
    Ratio of test to application code line changes
    Amount of devs
    Ratio test to app code
    [Buffardi et al., 2012] K. Buffardi and S. H. Edwards, “Impacts of Teaching Test-Driven Development to Novice Programmers,”
    International Journal of Information and Computer Science IJICS, vol. 1, no. 6, pp. 135–143, 2012.
    

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16. Last-Minute Commits
    ■ Expectation:
    high percentage of commits shortly before Sprint end
    ■ “Deadline-Driven development” [Ariely et al., 2002]
    16
    Percentage of Last-Minute Commits per developer
    [Ariely et al., 2002] D. Ariely and K. Wertenbroch, “Procrastination, deadlines, and performance: self-control by precommitment.”
    Psychological Science, vol. 133, pp. 219–224, 2002.
    Frequency
    Ratio of last-minute commits by developer
    

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17. Project Evidence vs. Perceptions
    17
    Correlations between perceptions and data
    TDD - Ratio Test/App Code
    CCO - Unique Files Edited
    Deadline-Driven Development - Last-Minute Commits
    “Check in early, check in often” - Avg. LOC churn
    Parallel User Stories - Unique User Story Identifiers
    Useful code reviews - Pull Request Comments
    

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18. Study Summary
    18
    A case study on agile practice usage in student teams
    ■ Case study within education context on selected
    Agile practice usage in teams
    ■ Initial measures for Agile practice
    ■ Self-assessments correlated with measurements
    concerning TDD and last-minute work
    □ Well-defined concepts, intuitive to grasp and measure
    □ Shared mental models
    

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19. 19
    ■ Differing assumptions between
    measurement creator and participants
    ■ Is employed proxy not measuring the intended construct or are
    perception and data at odds in this context?
    Starting points for discussion and improvement
    Conclusions & Interpretation
    Contributions and lessons learned in this study
    

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20. Future Work
    20
    Integrating project data analysis into SE processes
    Software Process Improvement
    

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21. Future Work
    21
    Integrating project data analysis into SE processes
    ■ Scrum Retrospective: “an opportunity for the Scrum Team
    to inspect itself” [Schwaber, 2017]
    ■ Common process problems, common diagnoses approaches
    ■ Project data: additional perspective on team development process
    ■ New Retrospective activities based on project data
    ■ e.g. Remedy Appraisal:
    did a (process) change manifest in project data?
    [Schwaber et al., 2017] Schwaber, K., & Sutherland, J., “The Scrum Guide - The Definitive Guide to Scrum: The Rules
    of the Game”, 2017, [online] Available: http://scrumguides.org/docs/scrumguide/v2017/2017-Scrum-Guide-US.pdf
    

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22. 22
    Retro
    Bot
    Vision
    

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23. Summary
    23
    

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24. Image Sources
    24
    In order of appearance
    ■ attitude by Nithinan Tatah from the Noun Project
    ■ Data by Alice Design from the Noun Project
    ■ agile by Florent B from the Noun Project
    ■ Mortar Board by Mike Chum from the Noun Project
    ■ developer by Becris from the Noun Project
    ■ GitHub mark by GitHub, Inc.
    ■ questions by Gregor Cresnar from the Noun Project
    ■ Survey by unlimicon from the Noun Project
    ■ Merge by Danil Polshin from the Noun Project
    ■ measures by supalerk laipawat from the Noun Project
    ■ sum by Trevor Dsouza from the Noun Project
    ■ end by priyanka from the Noun Project
    ■ Future by Alice Design from the Noun Project
    

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