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Using Developer Conversations to Resolve Uncertainty in Software Development: A Position Paper

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Using Developer Conversations to Resolve Uncertainty in Software Development: A Position Paper

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Presented at RSSE'14 at ICSE'14 in Hyderabad, India.

Authors:
Ahmed Shah Mashiyat, Michalis Famelis, Rick Salay, Marsha Chechik

Absract:
Software development is a social process: tasks such as implementing a requirement or fixing a bug typically spark conversations between the stakeholders of a software project, where they identify points of uncertainty in the solution space and explore proposals to resolve them. Due to the fluid nature of these interactions, it is hard for project managers
to maintain an overall understanding of the state of the discussion and to know
when and how to intervene. We propose an approach for extracting the uncertainty information from developer conversations in order to provide managers with analytics.
Using these allows us to recommend specific actions that managers can take to
better facilitate the resolution of uncertainty.

Presented at RSSE'14 at ICSE'14 in Hyderabad, India.

Authors:
Ahmed Shah Mashiyat, Michalis Famelis, Rick Salay, Marsha Chechik

Absract:
Software development is a social process: tasks such as implementing a requirement or fixing a bug typically spark conversations between the stakeholders of a software project, where they identify points of uncertainty in the solution space and explore proposals to resolve them. Due to the fluid nature of these interactions, it is hard for project managers
to maintain an overall understanding of the state of the discussion and to know
when and how to intervene. We propose an approach for extracting the uncertainty information from developer conversations in order to provide managers with analytics.
Using these allows us to recommend specific actions that managers can take to
better facilitate the resolution of uncertainty.

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Using Developer Conversations to Resolve Uncertainty in Software Development: A Position Paper

  1. 1. Ahmed Shah Mashiyat, Michalis Famelis, Rick Salay, Marsha Chechik University of Toronto RSSE@ICSE 2014 Using Developer Conversations to Resolve Uncertainty in Software Development: A Position Paper
  2. 2. Software Development is Social 2 Conversation between developers on jazz.net about an enhancement ticket
  3. 3. Software Development is Social 3 Conversation between developers on jazz.net about an enhancement ticket Ideal conversation: Question, Debate, Solution
  4. 4. The Manager’s Perspective Goals: • Product quality • Speedy delivery So, conversations should: • cover all issues • include all stakeholders • not take longer than necessary • But that is not always the case… 4 Manager
  5. 5. Software Development is Social 5 (The conversation has been abstracted and simplified for presentation purposes. The listing to the left is not intended to be legible.
  6. 6. Software Development is Social 6 “Ali” is leading the development of a component. (The conversation has been abstracted and simplified for presentation purposes. The listing to the left is not intended to be legible.
  7. 7. Software Development is Social 7 He seeks input from stakeholders. ` Color? Orientation?
  8. 8. Software Development is Social 8 Stakeholders start discussing. ? ? “Christine”
  9. 9. Software Development is Social 9 Stakeholders start discussing. ? ? “Bao”
  10. 10. Software Development is Social 10 Stakeholders start discussing. ? ?
  11. 11. Software Development is Social 11 Ali thinks he has found the answers he needs = = Bao agrees
  12. 12. Software Development is Social 12 Ali marks the issue as resolved = =
  13. 13. Software Development is Social 13 = = But “Dimitra” thinks that the issue is not settled Size??
  14. 14. Software Development is Social 14 = = But nobody takes her objection into account Size?? Guys?
  15. 15. Conversations are Messy • The decision about orientation was not debated • The question about size was left unanswered • Stakeholder Dimitra was not consulted until late • Changing the status to “resolved” was premature 15 Manager
  16. 16. Proposed Solution 16 Mine conversations Gather Analytics Generate Action Recommendations
  17. 17. Proposed Solution 17 Mine conversations Gather Analytics Generate Action Recommendations
  18. 18. Generating Recommendations Use metrics to support managers in facilitating conversations: 18 Intervene when questions are left unanswered for a “long” period of time Alert when questions are prematurely marked as resolved Report when a question has been discussed enough to be considered mature If a question is re-opened, create a context reminder of previously examined alternatives
  19. 19. Proposed Solution 19 Mine conversations Gather Analytics Generate Action Recommendations
  20. 20. • Online vs Offline (post-hoc) • Main challenge: determining resolution of a point of uncertainty: • Trusting status changes not necessarily a good idea • Who should be part of discussion? • People’s stance may not be obvious • E.g. does elaboration imply endorsement? • What does absence of comment (a person’s “default”) mean? • For now: Compute metrics from the structure of the conversation 20 Gathering Analytics about Uncertainty
  21. 21. Dashboard Mock-ups 21
  22. 22. Proposed Solution 22 Mine conversations Gather Analytics Generate Action Recommendations
  23. 23. Mining the Conversations 23 ` • Identify unique Points of Uncertainty from question utterances
  24. 24. Mining the Conversations 24 • Identify unique Points of Uncertainty from question utterances • Associate them with Proposed Alternatives
  25. 25. Mining the Conversations 25 • Identify unique Points of Uncertainty from question utterances • Associate them with Proposed Alternatives • Track the stakeholders’ arguments
  26. 26. Mining the Conversations • Identify unique Points of Uncertainty from question utterances • Associate them with Proposed Alternatives • Track the stakeholders’ arguments • Recognize the Resolution of Uncertainty 26
  27. 27. Construct a Model of the Conversation 27 • Twente Argumentation Schema (TAS) • Time: left-to-right, top-to-bottom
  28. 28. Constructing the TAS Model 28 Automation Challenges • Identifying unique points of uncertainty among many utterances of questions • Identifying proposals when they are not stated explicitly • e.g. cultural differences in assertiveness • Mapping utterances of proposals to points of uncertainty Benefits • Associates points of uncertainty with proposed answers • Easy to compute structural metrics • Can be used to trace uncertainty to software artifacts
  29. 29. Summary 29 quality speed
  30. 30. Future Work • How to best automate the creation of the TAS model? • How can we safely determine that a point of uncertainty has been resolved? • What are the most useful metrics, analytics and action recommendation for managers? • How to connect uncertainty in conversations with relevant organizational, social and software models? 30
  31. 31. Questions? icons by: 31

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