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Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
Story points considered harmful - or why the future of estimation is really in our past
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Story points considered harmful - or why the future of estimation is really in our past

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  • Galileo started by asking questions about what he saw...
  • Just like we should...
  • We’ve all been exposed to various estimation techniques. Just quickly: can you name a few? Expert estimationConsensus estimationOther complex estimation techniques like: Gantt, PERT, Function Point Analysis. Then we have cost vs time with cost techniques like: COCOMO, SDM, etc.And of course, the topic for today: Story Point Estimation.What do all of these have in common? They all look at the future. Why is this important?
  • Because looking at the future is always difficult. We humans are very good at anticipating immediate events in the physical world, but in the software world what we estimate is neither immediate, nor does it follow any physical laws that we intuitively understand!The indisputed fact is that we, humans are very bad at predicting the future. But that is not all! Lately and especially in the agile field we have been finding a new field of study: Complexity Sciences.
  • Retrospective Coherence...Fibonnaci series as an example of this
  • A field of study that tries to identify rules that help us navigate a world where even causality (cause and effect) are challenged. An example may be what you may have heard of: the Butterfly effect...Complexity Sciences are helping us develop our own understanding of software development based on the theories developed in the last few years. Scrum being a perfect example of a method that has used complexity to inspire and justify its approach to many of the common problems we face in Software development. Scrum has used self-organization, and ”emergence” as concepts in explaining why it’s approach works. However, there’s a catch.
  • What do you think of when you read this word? This is just a simple example of something that Complexity Sciences explore. In a complex environment we don’t have discernable causality!Some times this is due to delayed effects from our actions, most often it is so that we attribute causality to events in the past when in fact no cause-effect relationship exists (restrospective coherence). But, in the field of estimation this manifests itself in a different way. In order for us to be able to estimate we need to assume that causality exists (if I ask Tom for the code review, then Helen will be happy with my pro-activeness and give me a bonus). The fact is that in a complex environment, this basic assumption that we can assume causality is not valid! Without causality, the very basic assumption that justifies estimation falls flat!Cognitive ping-pong to re-inforce the ”complexity” point.
  • So, which is it? Do we have a complex environment in software development or not? If we do then we cannot at the same time argue for estimation (and build a whole religion on it)!And, if we are not in a complex environment we cannot then claim that Scrum, with it’s focus on solving a problem in the complex domain, can work!So then, the question for us is: Can this Story Point based estimation be so important to the point that its creator now claims exhorbitant amounts of money to teach you what either does not work, of fully invalidates the method (scrum) which he will also happily sell you? Luckily we have a simple alternative that allows for the existence of a complex environment and solves the same problems that Story Points were designed (but failed to) solve.
  • Not everything scales Not everything scales equallyNot everything scales linearly
  • The alternative to Story Point estimation is simple: Just count the number of Stories you have completed (done) in the previous iterations. They are the best indicator of future performance! Then use that information to project future progress. Basically, the best predictor of the future is your past performance!Can it really be that simple? To test this approach I looked at data from different projects and tried to answer a few simple questions
  • Here are the questions that I started with...
  • Exchange rate – how people transform SP’s into time (real – understandable)
  • Shelf-life and how long term estimates don’t work
  • The final question is really: why do we really estimate stories in Story Points and have built an entire religion with ceremonies, sacrifices, priests and a pope of Story Point estimation? The fact is that we don’t release Story Points! When all is said and done the question that me or you as a customer will be interested in answering is: does this product have the ”story” that I am interested in?
  • The final question is really: why do we really estimate stories in Story Points and have built an entire religion with ceremonies, sacrifices, priests and a pope of Story Point estimation? The fact is that we don’t release Story Points! When all is said and done the question that me or you as a customer will be interested in answering is: does this product have the ”story” that I am interested in?
  • Here are the questions that I started with...
  • Here are the questions that I started with...
  • Transcript

    • 1. Story Points Considered Harmful Or why the future of estimation is really in our past... #ard2012, Riga All pictures available on
    • 2. Vasco Duarte @duarte_vasco http://bit.ly/vasco_blog http://bit.ly/vasco_slideshare Talk Developed with @josephpelrine
    • 3. Tell me again: why did we move here?
    • 4. </intro> <talk>
    • 5. 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. Expert estimation Consensus estimation Function Point Analysis COCOMO
    • 7. Precognition [pree-kog-nish-uhn] pre·cog·ni·tion [pree-kog-nish-uhn] 1. knowledge of a future event or situation, especially through extrasensory means.
    • 8. (Hindsight is always twenty-twenty) -Anonymous (the other one!) Life Can only be understood backwards, but it must be lived forwards… - Soren Kierkegaard
    • 9. To be or not to be complex! That is the question!
    • 10. Looking for an alternative...
    • 11. • Q1: Is there sufficient difference between what Story Points and ’number of items’ measure to say that they don’t measure the same thing? • Q2: Which one of the two metrics is more stable? And what does that mean? • Q3: Are both metrics close enough so that measuring one (# of items) is equivalent to measuring the other (Story Points)?
    • 12. Data summary • Nine (9) data sets (a few more by now...) • I was not a stakeholder or had any role in any of these projects • Data came from different companies and different sized teams
    • 13. The Data spr22 spr21 spr20 spr19 spr18 spr17 spr16 spr15 0 5 10 15 20 25 30 35 40 45 Story pts done items done Correlation: 0,755 Team A / Company N spr21 spr20 spr19 spr18 spr17 0 5 10 15 20 25 30 35 40 45 Sp Normalized Items done normalized Correlation (w/out normalization): 0,92 Team CB / Company N spr F spr E spr D spr C spr B spr A 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 Sp Normalized Items done normalized Team HC / Company N Correlation (w/out) normalization: 0,83 spr14 spr13 spr12 spr11 spr10 spr9 spr8 spr7 0 10 20 30 40 50 60 Story pts done items done Team CF / Company N Correlation: 0,51 (0,71 without the spr14)
    • 14. The Data sprint 40 sprint 41 sprint 39 sprint 38 sprint 37 sprint 36 sprint 35 sprint 34 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Sp Normalized Items done normalized Team HCM / Company N Correlation (w/out normalization): 0,88 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 1 3 5 7 9 11 13 15 17 19 21 SP normalized Items Normalized Team AT / Company AT Correlation: 0,75 0 50 100 150 200 250 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Story point velocity Item velocity Correlation: 0,70 Team 2 / Company RF 0 20 40 60 80 100 120 140 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Story point velocity Item velocity Correlation: 0,75 Team 1 / Company RF
    • 15. The Data • What does this mean: – Q1: With so high correlation it is likely that both metrics represent a signal of the same underlying information. – Q2: The normalized data has similar value of Standard Variation (equaly stable). No significant difference in stability – Q3: They seem to measure the same thing so... 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0 10 20 30 40 50 60 70 80 # of stories Sum of SP Team AS / Company L Correlation (w/out normalization): 0,92
    • 16. We should analyse the claims that justify Story Points...
    • 17. • Claim 1: allows us to change our mind whenever we have new information about a story • Claim 2: works for both epics and smaller stories • Claim 3: doesn’t take a lot of time • Claim 4: provides useful information about our progress and the work remaining • Claim 5: is tolerant of imprecision in the estimates • Claim 6: can be used to plan releases • Source: Mike Cohn, User Stories Applied, page 87 More details at: http://bit.ly/ard2012_estimation
    • 18. Claim 1: allows us to change our mind whenever we have new information about a story • No explanation about what this means in the User Stories Applied book • Measuring completed number of items allows for immediate visibility of the impact of the new items in the progress (project burndown)
    • 19. Claim 2: works for both epics and smaller stories • Allowing for large estimates for items in the backlog does help to account for the impact of very large items by adding uncertainty. • 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.
    • 20. Story Points Hours
    • 21. Claim 3: doesn’t take a lot of time • 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” Silent Grouping technique: http://slidesha.re/AgileKonstanz_silentgrouping
    • 22. Claim 4: provides useful information about our progress and the work remaining • 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). • 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:
    • 23. 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. The names have been changed to protect the innocent...
    • 24. Sprint x Project feature burndown 0 20 40 60 80 100 120 140 Sprints NumberofFeaturesopen n. items done in this sprint (Actual Velo Projected Items still open at the end of Actual items open at the end of the spr (including results of removing items) Pilot feature burndown Pilot projection Ideal Pilot burndown Ideal Release Feature burndown 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
    • 25. Sprint x + 1 Project feature burndown 0 20 40 60 80 100 120 140 160 180 x x+1 x+2 x+3 x+4 x+5 x+6 x+7 x+8 x+9 x+10 x+11 x+12 Sprints NumberofFeaturesopen n. items done in this sprint (A Velocity) Projected Items still open at the sprint Actual items open at the end (including results of removing Pilot feature burndown Pilot projection Ideal Pilot burndown Ideal Release Feature burnd
    • 26. 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?
    • 27. Sprint x + 2 Project feature burndown 0 20 40 60 80 100 120 140 160 180 February M arch A pril M ay June JulyA ugust S eptem berO ctober N ovem ber D ecem berJanuaryFebruary M arch Sprints NumberofFeaturesopen n. items done in this spri Velocity) Projected Items still ope the sprint Actual items open at the sprint (including results o items) Pilot feature burndown Pilot projection Ideal Pilot burndown Ideal Release Feature b They did 10 items. A 20% increase in velocity.
    • 28. Finally...
    • 29. We release Stories/Backlog items, not story points...
    • 30. The Number of Items technique in a nutshell • 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! • 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)
    • 31. Why it works • By continuously harmonizing the size fo the Stories/Epics you are creating a distribution of the sizes around the median:
    • 32. • 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.
    • 33. One more thing...
    • 34. Q4: Which ”metric” is more accurate when compared to what actually happened in the project?
    • 35. A long project 24Sprints
    • 36. Which metric predicted most accurately the output of the whole project? a) After only the first 3 Sprints b) After only the first 5 Sprints
    • 37. Disclaimer... This is only one project! We need more data to verify or contradict these findings...
    • 38. After just 3 sprints # of Stories predictive powerStory Points predictive power The true oputput: 349,5 SPs completed The predicted oputput: 418 SPs completed +20% The true oputput: 228 Stories The predicted oputput: 220 Stories -4%!
    • 39. After just 5 sprints # of Stories predictive powerStory Points predictive power The true oputput: 349,5 SPs completed The predicted oputput: 396 SPs completed +13% The true oputput: 228 Stories The predicted oputput: 220 Stories -4%!
    • 40. Q4: Which ”metric” is more accurate when compared to what actually happened in the project?
    • 41. Click here! Sign-up and get the paper today! Sign-up and receive this paper which explains why we need #NoEstimates and how to get started! Includes: • Why estimates should not be used, and how they fail • An example of how #NoEstimates can reach a 4% accuracy to actuals • How to apply #NoEstimates: Vasco’s recipe!

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