Slides for the talk at the Second International Workshop on Software Engineering Education for Millennials (SEEM'18, http://seem2018.se-edu.org/), colocated with the 40th International Conference on Software Engineering (ICSE'18) in June 2018. Abstract: Using university capstone courses to teach agile software development methodologies has become commonplace, as agile methods have gained support in professional software development. This usually means students are introduced to and work with the currently most popular agile methodology: Scrum. However, as the agile methods employed in the industry change and are adapted to different contexts, university courses must follow suit. A prime example of this is the Kanban method, which has recently gathered attention in the industry. In this paper, we describe a capstone course design, which adds the hands-on learning of the lean principles advocated by Kanban into a capstone project run with Scrum. This both ensures that students are aware of recent process frameworks and ideas as well as gain a more thorough overview of how agile methods can be employed in practice. We describe the details of the course and analyze the participating students' perceptions as well as our observations. We analyze the development artifacts, created by students during the course in respect to the two different development methodologies. We further present a summary of the lessons learned as well as recommendations for future similar courses. The survey conducted at the end of the course revealed an overwhelmingly positive attitude of students towards the integration of Kanban into the course.

1. Scrum2Kanban: Integrating Kanban and Scrum in a
University Software Engineering Capstone Course
SEEM’18 @ ICSE’18, Gothenburg, Sweden
June 2018
Christoph Matthies
christoph.matthies@hpi.de
Enterprise Platform and Integration Concepts
Hasso Plattner Institute, University of Potsdam

2. Background
Undergraduate Agile SE Capstone Course
2

3. Background
Undergraduate Agile SE Capstone Course w/ Kanban
3

4. Research Questions
4
■ What are students’ perceptions of Kanban practices?
■ Are those perceptions accurate?
■ How does using Kanban influence workflows?

5. Method
Two different approaches
5
Survey
(N=18)
Development Artifact Analysis
(GitHub tickets & commits)
1 2

6. Survey
General Attitude
6
■ Positive attitudes towards including Kanban
■ Recommended
■ Good understanding of agile
■ Preferred over last Scrum week
■ Neutral towards additional lectures, high variance
Sanity Check

7. Survey
Extract of Attitudes towards Kanban
7
■ Advantages of Kanban

8. Survey
Extract of Attitudes towards Kanban
8
■ Advantages of Kanban
■ Drawbacks of Kanban

9. Survey
Extract of Attitudes towards Kanban
9
■ Advantages of Kanban
■ Drawbacks of Kanban
■ Change in User Stories / Requirements

10. Development Data Analysis
Artifacts
10
■ Length (title, body)
■ # Comments
■ # Interactions
■ Opened/Closed by
■ Assignee
Commit HistoryUser Stories
■ Count
■ Files changed
■ Insertions
■ Deletions
■ Merge?

11. Findings
User Stories
11
User Stories were shorter when using Kanban
■ Mean body length was lower (513 vs. 367 chars), but not titles
■ Support for perception of US contents
More dynamic interaction with US during Kanban
■ Only ~⅔ of user stories created by POs (vs 85%+ in Scrum)
■ Support for perception of autonomy
Uneven task distribution
■ Not fixed by Kanban, # unique assignees did not significantly change
■ Support for perception
■ Identified need for improvement

12. 12
More commits
■ More non-merge commits (138 vs 289)
■ Support for hypothesis
Smaller commits
■ Diff sizes similar
■ Hypothesis not validated
Problem with Merges in Kanban
■ Mean amount of merge commits per week almost tripled (52 to 142)
■ Support for perceptions
■ Need for improvement
Findings
Commits

13. Conclusions
13
■ Students’ software development data
■ Another dimension of analysis
■ Addition to surveys
■ Artifacts in SE always produced, already there
■ Not everyone fills out voluntary survey

14. 14
■ Students’ software development data
■ Another dimension of analysis
■ Addition to surveys
■ Artifacts in SE always produced, already there
■ Not everyone fills out voluntary survey
■ Contrasting perceptions and data can
reveal areas of improvement / further research
Conclusions

15. Summary

16. Image Credits
16
■ HPI Campus by Stephan Schultz (CC BY 2.0)
■ Survey by Vectors Market from the Noun Project (CC BY 3.0)
■ analysis by Alvaro Cabrera from the Noun Project (CC By 3.0)
■ Service Report by Sophia Bai from the Noun Project (CC BY 3.0)
■ Merge by Danil Polshin from the Noun Project (CC BY 3.0)
■ GitHub Mark by GitHub Inc. (https://github.com/logos)