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.

Story Points considered harmful – a new look at estimation techniques

1,963 views

Published on

Story Points are the typical estimation unit for Agile Teams. But do they really work, or are there better ways to estimate? In this talk, we‘ll look at the problem of estimating, as well as empirical data questioning the validity of story points, and we‘ll explore new techniques, based on cognitive psychology, chronobiology, and good old common sense, that will immediately help your teams estimate more accurately.

Published in: Technology, Education

Story Points considered harmful – a new look at estimation techniques

  1. 1. Story Points Considered Harmful Or why the future of estimation is really in our past... OOP 2012, Munich All pictures available on
  2. 2. Vasco Duarte @duarte_vasco http://bit.ly/vasco_blog Joseph Pelrine @josephpelrine www.metaprog.com/blogs
  3. 4. Tell me again: why did we move here?
  4. 9. </intro> <talk>
  5. 11. The Flat Earth Society (also known as the International Flat Earth Society or the International Flat Earth Research Society) is an organization that seeks to further the belief that the Earth is flat instead of an oblate spheroid More at : http://theflatearthsociety.org
  6. 12. Expert estimation Consensus estimation Function Point Analysis COCOMO SDM
  7. 13. Precognition [pree-kog-nish-uhn] pre·cog·ni·tion    [pree-kog-nish-uhn] <ul><li>knowledge of a future event or situation, especially through extrasensory means. </li></ul>
  8. 14. (Hindsight is always twenty-twenty) -Anonymous (the other one!) Life Can only be understood backwards, but it must be lived forwards… - Soren Kierkegaard
  9. 16. GREEN
  10. 17. To be or not to be complex! That is the question!
  11. 19. Looking for an alternative...
  12. 20. <ul><li>Q1 : Is there sufficient difference between what Story Points and ’number of items’ measure to say that they don’t measure the same thing? </li></ul><ul><li>Q2 : Which one of the two metrics is more stable? And what does that mean? </li></ul><ul><li>Q3 : Are both metrics close enough so that measuring one (# of items) is equivalent to measuring the other (Story Points)? </li></ul>
  13. 21. Data summary <ul><li>Nine (9) data sets </li></ul><ul><li>I was not a stakeholder or had any role in any of these projects </li></ul><ul><li>Data came from different companies and different sized teams </li></ul>
  14. 22. The Data Correlation: 0,755 Team A / Company N Team HC / Company N Correlation (w/out) normalization: 0,83 Correlation (w/out normalization): 0,92 Team CB / Company N Team CF / Company N Correlation: 0,51 (0,71 without the spr14) !!
  15. 23. The Data Team HCM / Company N Correlation (w/out normalization): 0,88 Correlation = 0,86 Team A / Company JO Correlation: 0,70 Team 2 / Company RF Correlation: 0,75 Team 1 / Company RF
  16. 24. The Data <ul><li>What does this mean: </li></ul><ul><ul><li>Q1: With so high correlation it is likely that both metrics represent a signal of the same underlying information. </li></ul></ul><ul><ul><li>Q2: The normalized data has similar value of Standard Variation (equaly stable). No significant difference in stability </li></ul></ul><ul><ul><li>Q3: They seem to measure the same thing so... </li></ul></ul>Team AT / Company AT Correlation: 0,75
  17. 25. We should analyse the claims that justify Story Points...
  18. 26. <ul><li>Claim 1: allows us to change our mind whenever we have new information about a story </li></ul><ul><li>Claim 2: works for both epics and smaller stories </li></ul><ul><li>Claim 3: doesn’t take a lot of time </li></ul><ul><li>Claim 4: provides useful information about our progress and the work remaining </li></ul><ul><li>Claim 5: is tolerant of imprecision in the estimates </li></ul><ul><li>Claim 6: can be used to plan releases </li></ul><ul><li>Source: Mike Cohn, User Stories Applied, page 87 </li></ul>
  19. 27. Claim 1: allows us to change our mind whenever we have new information about a story <ul><li>No explanation about what this means in the User Stories Applied book </li></ul><ul><li>Measuring completed number of items allows for immediate visibility of the impact of the new items in the progress (project burndown) </li></ul>
  20. 28. Claim 2: works for both epics and smaller stories <ul><li>Allowing for large estimates for items in the backlog does help to account for the impact of very large items by adding uncertainty. </li></ul><ul><li>The same uncertainty exists in any way we may use to measure progress. The fact is that we don’t really know if an Epic (say 100 SPs) is really equivalent to a similar size aggregate of User Stories (say 100 times 1 SP story). Conclusion: there is no significant added information by classifying a story in a 100 SP category. </li></ul>
  21. 29. Story Points Hours
  22. 30. Claim 3: doesn’t take a lot of time <ul><li>Not my experience. Although some progress has been done by people like Ken Power (at Cisco) with the Silent Grouping technique , the fact that we need such technique should dispute any idea that estimating in SP’s ”doesn’t take a lot of time” </li></ul>Silent Grouping technique: http://slidesha.re/AgileKonstanz_silentgrouping
  23. 31. Claim 4: provides useful information about our progress and the work remaining <ul><li>This claim holds if, and only if you have estimated all of your stories. Even the stories that will only be developed a few months or even a year later (for long project). This approach is not very efficient (Claim 3). </li></ul><ul><li>Basing your progress assessment on the Number of Items completed in each Sprint is faster to calculate (# of items in the PBL / velocity per Sprint = number of Sprints left) and can be used to provide critical information about project progress. Example : </li></ul>
  24. 32. <ul><li>The example you are about to see is a real life example. One where the data collected made a big impact on an important business decision. </li></ul><ul><li>The names have been changed to protect the innocent... </li></ul>
  25. 33. Sprint x Evolution of velocity Start of pilot/beta Release date Start of pilot/beta Actual progress trend What progress trend should be What progress trend should be
  26. 34. Sprint x + 1
  27. 35. The Velocity Bet Their history stated the following velocity evolution in the last 3 sprints: 1 8 8 They were learning the product and area in the first few sprints, which allowed for a ”getting-up-to-speed” assumption. Additionally they had committed to 15 items in the Sprint planning meeting. The product Owner stated that the R&D team would start doing 15 items per sprint (which would help them meet the goal of releasing the pilot and the release on time.) What was the result after the sprint?
  28. 36. Sprint x + 2 They did 10 items. A 20% increase in velocity.
  29. 37. Finally...
  30. 38. We release Stories/Backlog items, not story points...
  31. 39. The Number of Items technique in a nutshell <ul><li>When doing Backlog Grooming or Sprint Planning just ask: can this Story be completed in a Sprint by one person? If not, break the story down! </li></ul><ul><li>For large projects use a further level of abstraction: Stories fit into Sprints, therefore Epics fit into meta-Sprints (for example: meta-Sprint = 4 Sprints) </li></ul>
  32. 40. Why it works <ul><li>By continuously harmonizing the size fo the Stories/Epics you are creating a distribution of the sizes around the median: </li></ul>
  33. 41. <ul><li>Assuming a normal distribution of the size of the stories means that you can assume that for the purposes of looking at the long term estimation/progress of the project, you can assume that all stories are the same size , and can therefore measure progress by measuring the number of items completed per Sprint . </li></ul>
  34. 42. Vasco Duarte @duarte_vasco http://bit.ly/vasco_blog Joseph Pelrine @josephpelrine http://metaprog.com/blogs

×