Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

User stories: from good intentions to bad outcomes - AgiNext 2020

99 views

Published on

User stories are one of the most visible artefacts of most agile methods and have generated large quantities of expert advice. In my experience, much of that advice is often misinterpreted.

In this session, we’ll explore several classic pieces of advice, to see how misunderstandings can lead to bad outcomes, despite the best intentions. The advice we’ll look at relates to:

– an acronym: INVEST, created by Bill Wake
– a technique: relative estimation (as used in Planning Poker, created by James Grenning)
– a template: Connextra (As-A/I-Want/So-That), created by Rachel Davies

You’ll leave with a clearer appreciation of user stories and an understanding of just how context-sensitive most advice is.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

User stories: from good intentions to bad outcomes - AgiNext 2020

  1. 1. @sebrose h)p://smartbear.com Seb Rose User stories: from good intentions to bad outcomes
  2. 2. @sebrose h)p://smartbear.com As a <role> I want <goal/desire> So that <benefit> Template - Connextra
  3. 3. @sebrose h)p://smartbear.com Breakout room Discuss: • Why do user stories exist? • What is the Connextra template for? 5 minutes
  4. 4. @sebrose h)p://smartbear.com Placeholder for a conversa;on
  5. 5. @sebrose h)p://smartbear.com … to detailed small increments From placeholder for a conversa;on …
  6. 6. @sebrose h)p://smartbear.com The Connextra template has let us forget why user stories were invented
  7. 7. @sebrose h)p://smartbear.com I N V E S T Acronym
  8. 8. @sebrose h)p://smartbear.com Independent Nego;able Valuable Es;matable Small Testable I N V E S T Acronym
  9. 9. @sebrose h)p://smartbear.com Breakout room Valuable Define the meaning of : (in the context of user stories and INVEST) 5 minutes
  10. 10. @sebrose h)p://smartbear.com Minimum marketable feature (MMF)? What is value? Potentially shippable increment? Demonstrable functionality? How might these interact with “Small”???
  11. 11. @sebrose h)p://smartbear.com Value is anything that: - increases knowledge - decreases risk - generates useful feedback
  12. 12. @sebrose h)p://smartbear.com INVEST leaves plenty of room for misinterpretation
  13. 13. @sebrose h)p://smartbear.com Technique - story points
  14. 14. @sebrose h)p://smartbear.com - Have no units - Are rela;ve - Are team specific - Es;mated by people doing the work Story points - refresher
  15. 15. @sebrose http://smartbear.com Story points are rela;ve
  16. 16. @sebrose h)p://smartbear.com
  17. 17. @sebrose h)p://smartbear.com ... these studies which have for a few years now given rise to the claim that "research shows that people are better at relative than absolute estimation" do not in fact seem to square with that claim. This doesn't entail that relative estimation doesn't work - only that it is not proven. http://guide.agilealliance.org/guide/relative.html
  18. 18. @sebrose h)p://smartbear.com
  19. 19. @sebrose http://smartbear.com Is it small, or just far away?
  20. 20. @sebrose h)p://smartbear.com Story points continue to cause massive dysfunction
  21. 21. @sebrose h)p://smartbear.comhttps://ronjeffries.com/articles/019-01ff/story-points/Index.html Story points - apology
  22. 22. @sebrose h)p://smartbear.com hHps://esJmaJon.lunarlogic.io/assets/cards-range-8fc41b2e3fd282125f4602a712020204.png https://estimation.lunarlogic.io/
  23. 23. @sebrose h)p://smartbear.com Bad outcomes That doesn’t work here!
  24. 24. @sebrose h)p://smartbear.com Examples revisited
  25. 25. @sebrose h)p://smartbear.com No silver bullet Not only are there no silver bullets now in view, the very nature of software makes it unlikely that there will be any. Frederick P. Brooks "No Silver Bullet: Essence and Accidents of Software Engineering,"  Computer, Vol. 20, No. 4 (April 1987) pp. 10-19
  26. 26. @sebrose h)p://smartbear.com PaNerns can help us filter •Name •Problem •Context •Forces •Solution •Resulting Context •Known Uses •Related Patterns •http://wiki.c2.com/?CanonicalForm •Context •Forces
  27. 27. @sebrose h)p://smartbear.com Takeaways Understand the “Why”Always consider your context Bad outcomes don’t mean the advice was bad
  28. 28. Seb Rose Twitter: @sebrose Blog: http:/cucumber.io/blog E-mail: seb@smartbear.com http://bddbooks.com

×