An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives

Slides for the talk on "An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives", held at the 1st International Workshop on Bots in Software Engineering on May 28th, 2019 in Montreal, Canada, in conjunction with ICSE 2019.

Paper authors: Christoph Matthies, Franziska Dobrigkeit, Guenter Hesse
Website: https://botse.github.io/
Preprint: https://arxiv.org/abs/1903.02443

Transcript

1. Hasso Plattner Institute University of Potsdam, Germany christoph.matthies@hpi.de @chrisma0 An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives Christoph Matthies, Franziska Dobrigkeit, Guenter Hesse BotSE @ ICSE’19, Montréal, Canada, May 2019
2. Motivation 2 Why ChatBots? ▪ Chat solutions widely used in software teams ▪ Teams are often distributed ▪ Bots ➞ “virtual team members” [Lebeuf et al., 2017] □ Remote team member who prefers texting over video calling □ Give new team member a role that is currently often not filled: measurement and analysis □ Second set of eyes for feedback [Lebeuf et al., 2017] Lebeuf, C. & Storey, M.-A. & Zagalsky, A., “How Software Developers Mitigate Collaboration Friction with Chatbots”, Talking with Conversational Agents in Collaborative Action Workshop @ CSCW'17, 2017.
3. Idea 3 Why Retrospectives? ▪ Chatbots for software development teams □ Data produced during regular dev. activities [deSouza et al., 2005] □ Manual analysis is tedious, doesn’t scale well □ Assign the bot the analysis of this data ▪ What should the bot measure exactly? ▪ How does it integrate into existing processes? [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, New York, New York, USA: ACM Press, 2005, https://doi.org/10.1145/1099203.1099239.
4. Application Context 4 The Scrum Retrospective Meeting ▪ Scrum’s dedicated feedback and improvement meeting [Schwaber et al., 2017] [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
5. The Scrum Retrospective 5 An Ideal Habitat for an Analysis Software Bot Retrospective Meeting Sprint ▪ What went well? ▪ What should be improved next iteration?
6. The Scrum Retrospective 6 An Ideal Habitat for an Analysis Software Bot Did we improve what we planned? Retrospective Meeting
7. The Scrum Retrospective 7 An Ideal Habitat for an Analysis Software Bot Did we improve what we planned? Retrospective Meeting ▪ “decisions to optimize [..] based on the [...] state of the artifacts” - Scrum Guide [Schwaber et al., 2017] ▪ “Start with the hard data” [Esther et al., 2007] [Schwaber et al., 2017] Schwaber, K., & Sutherland, J, “The Scrum Guide - The Definitive Guide to Scrum: The Rules of the Game”, 2017. [Esther et al., 2007] Esther, D., & Larsen, D, “Agile retrospectives - Making Good Teams Great”, Journal of Product Innovation Management, Vol. 24, Pragmatic Bookshelf, 2007.
8. The Scrum Retrospective 8 An Ideal Habitat for an Analysis Software Bot Did we improve what we planned? commits, reviews test runs tickets static analysis Retrospective Meeting Project Data Evidence of last iteration’s work
9. The Scrum Retrospective 9 An Ideal Habitat for an Analysis Software Bot Did we improve what we planned? commits, reviews test runs tickets static analysis Retrospective Meeting Project Data Evidence of last iteration’s work
10. The Scrum Retrospective 10 An Ideal Habitat for an Analysis Software Bot Did we improve what we planned? commits, reviews test runs tickets static analysis Retrospective Meeting Project Data Evidence of last iteration’s work
11. Software Project Data 11 Mining Repositories of Teams code code analyses Project Data documentation Primary purpose: Communication Opportunity: Process Insights ...
12. Software Project Data 12 Mining Repositories of Teams ▪ Project data is continuously produced ▪ Holds insights into team processes code code analyses Project Data documentation Primary purpose: Communication Opportunity: Process Insights ...
13. RetroBot Workflow 13 The Steps and Resources which are Required Chat Context Developer Bot (1) (5) Analysis Artifact Measurements (3) (4) Analysis Results Bot Context Software Project Data (2) Team in retrospective RetroBot requires knowledge of team project data and of defined artifact measurements
14. Related Work 14 An Entire Family of Software Bots ▪ Tools for supporting Retrospectives through automation □ Reminders, archiving action items [goReflect, 2019] □ Facilitating activities [Retrium, 2019] □ Running surveys [Standuply, 2019] ▪ Chat platforms can support agile teams in Retrospectives ▪ Existing bots automate organizational tasks, inputs are solely team members’ perceptions [goReflect, 2019] GoReflect, “goReflect - Continuous Retrospectives for Agile Improvement,” 2019, [Online] Available: https://www.goreflect.com/ [Retrium, 2019] Retrium, “The era of boring retrospectives isover,” 2019, [Online] Available: https://www.retrium.com [Standuply, 2019] Standuply, “Retrospective Meeting Slack Bot,” 2019, [Online] Available: https://standuply.com/retrospective-meeting
15. Vision 15
16. Summary 16
17. Image Credits 17 In order of appearance ▪ Robot by Oksana Latysheva from the Noun Project (CC BY 3.0 US) ▪ Retrospective meeting by Shocho from the Noun Project (CC BY 3.0 US) ▪ Developer by shashank singh from the Noun Project (CC BY 3.0 US) ▪ Wall by Creaticca Creative Agency from the Noun Project (CC BY 3.0 US) ▪ Gears by Icon Fair from the Noun Project (CC BY 3.0 US) ▪ Code by Gregor Cresnar from the Noun Project (CC BY 3.0 US) ▪ Documentation by tom from the Noun Project (CC BY 3.0 US) ▪ Analysis by Chameleon Design from the Noun Project (CC BY 3.0 US)