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Background
My team current is currently in the "bug fixing and polishing" phase of shipping a major rewrite. We still have a large stack of bugs to fix, scheduled against a couple of milestones. We've been asked to come up with estimates, of how much engineering effort is required to fix the bugs for each milestone.
For previous milestones, we've followed the following process:
Assign the bugs to the people that know the most about that area of the code, and will likely be the one to fix the bug.
Have each person go through the bugs that are assigned to them, and estimate how long they think it will take to fix the bugs, at an hour-level granularity. If a bug looks like it will potentially take more than a day or two to fix, they break the bug into likely subtasks, and estimate those.
Total the amount of work assigned to each person for each milestone, and try and balancing things out if people have drastically different amounts of work.
Multiply each person's total for each milestone by a "padding factor", to account for overly optimistic estimates (we've been using 1.5).
Take the largest total across the team members for a given release, and make that the time it will take for the team to close the existing bugs.
Estimate the number of bugs we expect to be created during the time it takes us to reach a particular milestone, and estimate how long on average, we think it will take to close each of these bugs. Add this on to the time to close the existing bugs for each release. This is our final number of the amount of work needed, delivered as a date by which we'll definitely ship that milestone.
This has been fairly accurate (we've come in pretty much spot on on our previous three milestones), but it's rather time consuming.
Current Problem
We've been asked to give estimates of the engineering time for upcoming milestones, but asked not to use the above process because it's too time consuming. Instead, as the tech lead of the team, I've been asked to provide estimates that are less certain, along with a certainty interval (ie, 1 month, plus or minus a week).
My primary estimation experience is with some variation of the method I described above (from a background of freelancing for a number of years). I've found that when I "shoot from the hip" on large tasks, I tend to be way off. I suspect it will be even worse when estimating how long it takes to fix bugs in areas of the code I don't know very well.
What tips, tricks or techniques have you found successful for estimating quickly, without breaking things down into fine grained tasks and estimating them?
Things that are not an option:
Not giving an estimate - I've tried this, it didn't fly:)
Picking a number and confidence interval that is ridiculously wide - I've considered this, but I don't think it'll fly either.
Evidence-base scheduling - We're using JIRA, which doesn't have any evidence-base scheduling tools written for it, and we can't migrate to FogBugz currently (BTW, if someone goes and writes an evidence-based scheduling plugin for JIRA, we would gladly pay for it).
The best tip for estimating: round up a heck of a lot.
It sounds like you're already an expert on the topic of estimation, and that you know the limitations of what's possible. It just isn't possible to estimate a task without assessing what needs doing to complete it!
Amount of time assessing is directly proportional to accuracy of estimate. And these things converge at the point when time assessing is so accurate you've solved the task, at that moment, you know exactly how long it takes.
Hmm, sorry, this may not be the answer you wanted to hear... it's just my thoughts on it though.
Be prepared to create a release at any time
Have the stake-holders prioritise the work done
Work on the highest priority items
Step 1. means you never miss a deadline.
Step 2. is a way of turning the question back on those who are asking you to estimate without spending time estimating.
Edit...
The above doesn't really answer your question, sorry.
The stake holders will want to prioritize work based on how long and expensive each task will be, and you are likely to be asked which of the highest prioritized changes you expect to be able to complete by the next deadline.
My technique that takes the least time is to use three times my impression of how long I think it would take me to do it.
You're looking for something taking longer than that, but not as long as your previous excellent estimates.
You'll still need to look at each bug, even if only to take a guess at whether it is easy, average, or tricky, or 1,2,4,8,16 or 32 hours work.
If you produce some code complexity metrics over your code base (eg cyclomatic complexity), and for each task, take a stab at which two or three portions of that code base will need to be changed the most, then estimate based on the assumption that the less complex portions of code will be quicker to change than the more complex portions. You could come up with some heuristics based on a few of your previous estimates, to use for each bug fix, giving an estimate of the time and variability required.
How about:
estimate=(bugs/devs)xdays (xK)
As simple as this is it's actually quite accurate. And can be estimated in 1minute.
It's confidence level is less than your detailed method, but I'd recommend you check your data on the last three milestones and check the difference between this quick estimate and your detailed estimate that will give you a "K" value representing your team's constant.
Be surprised.
Use Planning Poker, see the answers to How to estimate the length of a programming task
In simplest terms:
Your Most Absolutely Liberal Estimation * 3 = Your Estimate
The above may look like a joke, but it's not. I've used it many times. Time estimation on software projects of any kind is a game, just like making a deal with a car dealer. That formula will get you something to provide your management in a pinch and give you some room to play with as well.
However, if you're somehow able to get down to the more granular details (which is really the only way you'll be able to be more accurate), Google on Function Point Analysis, sometimes called "Fast Function Point Analysis" or "Fast Function Point Estimation".
Many folks out there have a myriad of spreadsheets, PDF's and the like that can help you estimate as quickly as possible. Check out the spreadsheets first as they'll have formulas built in for you.
Hope this helps!
You've been asking how to produce an estimate and an uncertainty interval. A better way to think of this is to do a worst-case estimate and a best-case estimate. Combine the two to have an estimate range. Well understood issues will naturally be more specific then the estimates for less-understood issues. For example, an estimate that looks like 1.5-2 days is probably for a well understood issue, an estimate that looks like 2-14 days would be typical for an issue not at all understood.
Limit the amount of investigation and time spent producing an estimate by allowing for a wider gap between the estimates. This works because its relatively easy to imagine realistic best case and worst case scenarios. When the uncertainty range is more than you're comfortable dealing with in the schedule, then take some time to understood the less understood issues. It may help to break them up.
I usually go for half-day granularity rather than hour granularity in my estimates if the work is expected to take more than a week overall.
public static class Time
{
/// <summary>
/// Estimates the hours.
/// </summary>
/// <param name="NumberPulledFromAss">The number pulled from ass.</param>
/// <param name="Priority">The priority.</param>
/// <param name="Complexity">The complexity.</param>
/// <returns>
/// a number in hours to estimate the time to complete a task.
/// Hey, you will be wrong anyway why waste more time than you need?
/// </returns>
public static int EstimateHours(int NumberPulledFromAss, int Priority, int Complexity)
{
var rand = new Random(NumberPulledFromAss);
var baseGuess = rand.Next(1, NumberPulledFromAss);
return (baseGuess + (Priority * Complexity)) * 2;
}
}
Your estimates are as accurate as the time you put into them. This time can be physical time breaking down the problem or drawing upon past experiences in areas you're familiar. If this isn't an option the try breaking the bugs/polish down into groups.
Trivial fix of a few hours.
Up to one day effort.
Very complex - one week effort.
Once you have these categorised then you can work out a rough guestimate.
Many hints may be useful in this article on an agile blog: Agile Estimating.
Calculating the variability in your estimate will take longer than calculating your estimate.
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I'm working in a team that's been consistently and fairly successfully working in an agile approach, and this has been working great for the current project until now, for our initial work, as we incrementally build the product.
We're now moving into the next phase of this though, and the management are keen for us to set some specific deadlines ourselves, for when we'll be in a position to demo and sell this to real customers, on the order of months.
We have a fairly well organised large backlog for each of the elements of functionality we'd like to include, and a good sense of the prioritisation of these individual bits of functionality.
The naive solution is to get the minimum list of stories that would provide a demo-able product, estimate all of those individually, and add them up and combine with our velocity to get a date, and announce we'll be demoing from then. That leaves no leeway though, and seems likely to result in a mad crunch as we get up to deadline time, which I desperately want to avoid.
As an improvement, I'd like to add in some ratio of more optional stories to act as either contingency or bonus improvements, depending on how we progress, but we don't have any idea what ratio would be sensible, or whether this is the standard approach.
I'm also concerned by having to estimate the whole of our backlog all in one go up-front, as that seems very time consuming, and it seems likely that we'll discover more information in the months before we get to that story, which will affect our estimates.
Are there recommended approaches to dealing with setting deadlines to allow for an agile development process? Most of the information I've seen seems to be around handling the situation once you've got a fixed deadline to hit instead. I'd also be interested in any relevant literature or interesting blog posts that cover this issue.
Regarding literature: the best book I know regarding the estimation in software is "Software Estimation: Demystifying the Black Art" by Steve McConnel. It covers your case. Plus, it describes the difference between estimation and commitment (set-deadline, in other words) and explains how to derive the second from the first reliably.
The naive solution is to get the minimum list of stories that would
provide a demo-able product, estimate all of those individually, and
add them up and combine with our velocity to get a date, and announce
we'll be demoing from then. That leaves no leeway though, and seems
likely to result in a mad crunch as we get up to deadline time, which
I desperately want to avoid.
This is the solution I have used in the past. Your initial estimate is going to be off a bit so add some slack via a couple of additional sprints before setting your release date. If you get behind you can make it up in the slack. If not, your product backlog gives you additional features that you can include in the release if you so choose. This will be dependent on your velocity metric for the team though. Adjust your slack based on how accurate you feel this metric is for the current team. Once you have a target release you can circle back to see if you have any known resource constraints that might affect that release.
The approach you describe is likely to be correct. You may want to estimate for all desirable features, and prioritise UI elements (because investors and customers basically just see the shiny UI), and then your deadline will be that estimated date for completion; then add on some slack in the form of scaling your estimates. Use the ratio between current productivity and your worst period to create a pessimistic estimate. You can use that same ratio to scale shorter estimates (e.g. for your estimate to the minimum feature set).
I'm working on a project with many unknowns like moving the app from one platform to another.
My original estimations are way off and there is no way I can really know for sure when this will end.
How can i deal with the inability to estimate such a project. It's not that I'm adding a button to a screen or designing a web site, or creating and app or even fixing bugs. These are not methods with bugs, these are assumptions made in the overall code, which are not correct anymore and are found step by step and each analyzed and mitigated with many more unknowns.
I happened to write a master thesis about software-estimation and there are lessons I've learned:
-1st Count, 2nd compute, 3rd judge - this means: first try to identify items in your work which are countable e.g files, classes, LOCs, UIs, etc. Then calculate using this data the effort (in person/days). Use judgement as the last ressort.
-Document your estimation! Show numbers. This minimizes your risk, thus you will present results not as your opinion, but as more or less objective figures. (In general, the more paper the cleaner the backside)
-Estimation is not a commitment. Commitment is one number, estimation is always a range - so give your estimation as a range ( use cone of uncertainty to select the range properly http://www.construx.com/Page.aspx?hid=1648 )
-Devide: Use WBS, devide your work in small pieces and estimate them separately. The granulity depents on the entire length, but at most a working-package soultn't be bigger than 10% of entire effort.
-Estimate effort first, then schedule, then costs.
-Consider estimation as support for planing, reestimate on each project phase (s. cone of uncertainty).
I would suggest the book http://www.stevemcconnell.com/est.htm which deals all these points, in particular how to deal with bosses, who try to pull a commitment from you.
Regards,
Valentin Heinitz
There's no really right answer for coming up with an accurate estimation, because there's no way to know it.
as for estimating the work itself, think about how each step can be divided into separate sub-steps, and break those down even smaller, until you can get a fair picture of as much of the work as you can, with chunks small and discreet enough to give sound estimates for. If you can, come up with both an expected time and a worst-case time, to get a range of where you could land.
Another way to approach this is to ignore the old system. It sounds like a headache. Make an estimate of scraping the old system and implementing a new one from scratch, or integrating a 3rd party, off the shelf solution. If there's a case to be made for this, it is worth at least investigating it.
Sounds like a post for postsecret not SO. :)
I would tell him that it will be done when its done, and if thats not good enough, he can learn to program and help you. Then again, I think that you might get fired, but hey that sounds like it might be better.
Tell him more or less what you told us. The project is too volatile too give an accurate estimate and the best you can do is give an estimate for a given task. As long as the number of tasks is unknown so will be the estimate. If he is at all worth his salary he would rather hear this than some made up number. This is not uncommon when dealing with a large legacy code base.
It's not that I'm adding a button to a screen or designing a web site,
or creating and app or even fixing bugs.
That is a real problem. You can not estimate what you don't have experience in. The only thing you can do is pad your estimate until you think it is a reasonable amount of time. The more unknowns you think there are the more you pad. The less you know about it the more you pad.
I read the below book and it spoke at length about accuracy vs precision. Basically you can be accurate but have a very large range. For instance you can be certain the task will be between 1 day and 1 year to complete. That is not very precise but it is really accurate.
Software Estimation Demystifying...
Some tips for estimating
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I have been using agile approaches (XP and Scrum) for my projects for several years with great results. But in all cases, all members of the dev team were committed 100% to the project.
Now I am faced with doing this when the team is not stable. For instance, one iteration there may be four people working, the next maybe only two or three.
I realize this makes it hard (or impossible) to estimate using the normal velocity approach since it will fluctuate to much and not be stable. What follows is that one cannot really expect to be able to release at the end of each iteration.
Maybe another approach is needed here. Just grab stuff from the backlog and just muddle through and release whenever it is possible. I really don't like that though...
Any thoughts?
From the question I assume you have some developers (probably, 2) 100% commited to the project and some (another 2-3) only participate at a times.
One thing you can do is set different process for core developers who are 100% commited and everyone else. Use you normal agile process for core people and release their work at normal iteration cycle. For non-core people, do little planning and assume their (and your) estimates would be way of at a times. Ideally their changes should be isolated and merged into stable branch of code by core members, but not every project's architecture and team roles allow this.
The point is to separate and isolate source of chaos and leave the heart of a project and team unaffected.
Maybe instead of agile approaches, you can slow things down with other iterative and incremental approaches. Instead of having iterations measured in weeks, having longer iterations (perhaps measured in months) would be better if you keep adding and dropping people from the team.
This doesn't mean that you still can't use some Agile techniques. I would still maintain your Backlogs and burn down charts, with the realization that instead of having a release every 2 weeks, you'll release every 6 weeks (~2 months). If you have new developers joining more experienced developers, use pair programming, assign the new developers to bug fixes, or assign the new developers to maintaining unit tests to help them learn the code base.
Velocity is only an estimation.
Naively, if you have a given velocity v with a team of 4 developers, then schedule your iteration with a velocity of (v/4)*number_of_developers
You can fudge this value if the members you are losing are particularly stronger or weaker than the average.
This is basically what PivotalTracker does with its team strength metric.
So, you have a project with a continually changing team size and your boss wants you to give him an accurate estimate of how long it will take? You can do this, as long as you keep in mind the difference between accurate and precise. Your precision will depend largely on the number of items and how granular (decomposed) each item is; the more items you have the more the Law of Large Numbers works for you, averaging out over- and underestimates.
Your accuracy is a function of confidence. Note that estimates aren't single-point values, they're a range with numbers with a percentage of confidence. For instance, a proper estimate wouldn't be "2 weeks" it would be "50% confidence of 2 weeks, 80% confidence of 4 weeks."
If I were the person assigned with the unenviable task of providing an estimate to completion for a project being managed as arbitrarily as in the original post, I'd try to figure out a range based upon the minimum number of folks assigned (e.g., "48 to 66 weeks given 2 developers [50% to 80% confident]"), and a range associated with the average number of folks assigned (e.g., "25 to 45 weeks with 5 developers [50% to 80% confident]"), and use the low figure from the average number along with the high figure from the minimum number (e.g., "25 to 66 weeks given anywhere from 2 to 5 developers [50% to 80% confident]"), and even then I'd put a disclaimer on it ("plus 10% for the lost time due to context switching").
Better yet, I'd explain exactly why this arrangement was, to be polite, sub-optimal, and why multi-tasking is a primary signpost on the road to project Hell.
As someone else suggested, changing the workflow from iteration-based to flow-based (Kanban) might well be a good strategy. With Kanban you handle changing project priorities by changing the priority of items in the backlog; once an item has been grabbed by the team it is generally finished (flows all of the way through the workflow, stakeholders aren't allowed to disrupt the team by screwing around with work-in-progress). I've used Kanban for sustained engineering projects and it worked very well. Re how it would help with estimates, the key to continuous flow is to try to have each work item be roughly the same size (1x, 2x, 3x, not 10x, 20x, 100x). You should track movement of items through the workflow by tracking dates of process state changes, e.g., Queue 1/15, Design 1/22, Dev 1/24, Test 2/4, Integrate 2/7, etc., and then generating a cumulative flow diagram regularly to evaluate the time-in-state durations over time. Working out how long the project should take given that you know the size of each item and the time through the workflow for items is a trivial computational exercise left to the reader. (The more interesting question is how to spot constraints... and then how to remove them. Hint: look for long times in states, because work piles up in front of constraints.)
Let the individual developer that will be working on the story estimate the effort required to complete the story. You can take into account historical variances in that developer's estimations, but the idea is that you can take their estimates and then figure out how many stories you'll be able to finish in that sprint.
Don't forget that average velocity is largely used for lookahead release planning; the team is responsible for selecting in each iteration how many backlog items to take on (although knowing historical velocity can assist them).
If your team size (and hence velocity) is fluctuating from iteration to iteration, you can still do useful release planning by using average velocities over the past N sprints, assuming the team fluctuations will continue and hence their long-term average velocity will actually be stable.
Your main problem here is that the team will find it hard to give predictable estimates and deliveries since the team is changing from sprint to sprint. This can also hurt the team commitment and continuous improvement.
This case might actually be well suited for a Kanban approach. Check out Henrik Knibergs introduction to Kanban for a quick overview.
Good luck!
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Suppose you are a project manager. You can estimate an effort in days for specific task for specific developer. After performing estimation you obtain some min and max values.
After this you delegate a task to developer. Actually you also set up deadline.
Which estimation is better to use when set up deadline: min or max?
As I see min estimation can result in stress for developer, max estimation can result in using all the time which is allocated to developer even if task can be complete faster (so called Student syndrome).
Which other pros and cons of two approaches?
EDIT:
Small clarification: I speak about setting up deadlines for subordinates when delegating the task, NOT for reporting to my boss.
EDIT:
To add one more clarification: I can keep in mind my real estimation, provide to boss slightly larger estimation, to subordinates - slightly smaller.
And this questions touches the following thing: is it good idea to provide to developer underestimation to make him working harder?
You should use the best guess which is a function of the min and max estimates* - not just the simple average -
best_guess = (min * min_weighting + max * max_weighting) / divisor*
* Tom Neyland suggests it should be (min_weighting + max_weighting). Actually I'm not sure whether that is correct, but it's probably more correct than my original divisor of 2.0.
The weighting you give to the min and max values will depend on the complexity of the task, the risks associated with the task, the likelihood of the risks occuring, the skill of the developer, etc. and will vary from organisation to organisation and from project to project. If you keep a record of your previous estimates and the actual time each took you'll be able to refine these estimates over time.
You should also use these values, plus a confidence value, when talking to senior management and customers. While giving the max and delivering early is not the same as giving the min and delivering late, it still shows that you don't have control over your development.
Giving the confidence value and an idea of the risks will also help manage expectations so if there are problems they're not unexpected.
* These min and max estimates will be got by various means - asking the developers, past experience etc. If polling developers then the actual min and max values should be treated as outliers and either discarded or modified in some way. What I mean here are the values you get from phrases like "it'll take 2 weeks if all goes well or a month if we hit some snags". So the values you plug into the formula are not the raw numbers.
Use neither min nor max but something in between.
Erring on the side of overestimation is better. It has much nicer cost behavior in the long term.
To overcome the stress due to underestimation, people may take shortcuts that are not beneficial in the long term. For example, taking extra technical debt thast has to be paid back eventually, and it comes back with an interest. The costs grow exponentially.
The extra cost from inefficiency due to student's syndrome behaves linearly.
Estimates and targets are different. You (or your managers and customers) set the targets you need to achieve. Estimates tell you how likely you are to meet those targets. Deadline is one sort of target. The deadline you choose depends on what kind of confidence level (risk of not meeting the deadline) you are willing to accept. P50 (0.5 probability of meeting the deadline) is commonplace. Sometimes you may want to schedule with P80 or some other confidence level. Note that the probability curve is a long-tailed one and the more confidence you want, the longer you will need to allocate time for the project.
Overall, I wouldn't spend too much time tracking individual tasks. With P50 targets half of them will be late in any case. What matters most is how the aggregate behaves. When composing individual tasks estimates into an aggregate, neither min or max is sensible. It's extremely unlikely that either all tasks complete with minimum time (most likely something like P10 time) or maximum time (e.g. P90 time): for n P10/P90 tasks the probability is 0.1^n.
PERT has some techniques for coming up with reasonable task duration probability distributions and aggregating them to larger wholes. I won't go into the math here. Here's some pointer for further reading:
Steve McConnell: Software Estimation - Demystifying the Black Art. It's quite readable and pragmatic but at least the 1st edition I have has some quirks in its math and otherwise.
Richard D. Stutzke: Estimating Software-Intensive Systems - Projects, Products and Processes. It's a little more academic, harder read but for example explains the math better.
Ask for best, likely and worst case scenario estimates instead. Then use Program Evaluation and Review Technique. However you may want to take a look at some PERT critique first.
For individual tasks or tasks making up the critical path it’s simply not prudent to go for the best case estimates. It’s like saying that the project is absolutely free of any risk and uncertainty. If the actual job turns out to be anything but the best case scenario you’ll end up blowing the schedule. It’s better to end up with some extra time on your hands and fill the time by implementing some nice-to-haves as opposed to having to work nights and weekends.
On the other hand if managers mostly went for the worst case estimates and in software world they can easily be an order of magnitude greater than the best case figures most projects would never make it past the feasibility and planning stage. Not all of the risks going to materialise.
Going for the best case estimate won't help fighting student syndrome. Include interim milestones and deliverables instead, beside being helpful at combating the student syndrome they're pre-requisite for having a trustworthy data on the project progress and uncovering early any potential issues.
If the difference between min and max are big rather than using some black magic formula I think it the best thing to do would be to go back to the developers and ask them to do a finer breakdown and prototyping, which will lead to better estimates where the gap between min and max is not that big.
Note to the question: In my opinion, the estimates should be done by the developers/architects since they have the best technical knowledge to be able to break down into tasks and estimate those tasks.
If you are estimating for a specific developer, and you know your estimates are generally accurate for that developer, then the min value is the logical deadline (initially). In the course of the project you will adjust deadlines according to circumstance.
If you have little experience with a specific developer, one of my fondly regarded previous managers would ask the developer himself to do the estimate and set the initial deadline a third of the distance between that developer's min and max, challenging the developer to beat it.
Something which has been missing in many of these answers (perhaps because it's slightly off-topic) is frequent updates. With younger/newer developers this is even more important - read the code they commit, and/or check in daily to ask them for specific, detailed reports.
This also allows you to set tight deadlines for developers without giving them too much stress, because they will know you're around to help adjust deadlines when needed.
Frequent updates give you the most important tool in setting customer/management expectations - early warning of issues which might delay things, and I prefer having that over any formula.
Is the developer going back into a cave to develop this or is there a good chance of changing requirements over the course of the project? I would think most projects will have a good chance that something won't go smoothly and thus it may be better to try to get the prototype up sooner rather than later.
As for the initial question, I think I'd break this out into a few different outcomes and consider each:
Gross underestimation -> This leads to the problem that there is still a lot of work to do and the manager appears unable to make reasonable estimations.
Minor underestimation -> In this case, either there is an extension, scope gets cut or some bugs are in the release, but this is better than the previous case.
Made the deadline, on time and on budget with quality -> While this may seem optimal as everything worked out, I don't think this is the best result possible.
Minor overestimation -> In this case, there is some breathing room that means either things finish early or some extra work is added. A point here is that this may seem to deliver a slightly better result than the previous case like how some companies will try to beat the earnings estimate by a small amount to do better than expected.
Gross overestimation -> I think this would be the worst case outcome though it is similar to the first in terms of someone being way out of their league in being able to provide a reasonable estimation.
That's just my opinion on each and others may have a different take on it than me.
If you're trying to hold developers to their minimum estimate, that's foolish. No one, in any industry, consistently hits their minimum time estimate for getting something done. Eventually, they'll just learn to pad their minimum estimates significantly, and then they'll never hit the old minimums, because all estimates will be above that.
In Agile/Scrum, you don't set firm deadlines, but set "how many hours left on this task". Every day, you update the amount of time left. You do not track hours spent, but do track estimated hours remaining, and you try and stay honest about it.
If you have lazy developers, this is bad, because they can easily game that system. If you have developers that are worth their salt, this is great. They get better at estimation pretty quickly, and you - as a project manager - learn how reliable their estimates are, and you'll have a much better feel for what estimates to pass up the chain based on the individual developer estimates.
Go slightly towards Agile, fire the bad developers as you discover which are which, reward the good developers for actually giving a damn, and have a more productive, happier team while being able to report more accurate expectations to your superiors.
If in doubt under promise and over deliver: you want to be the person who is delivering more than they were expecting, not less. Based on this always go with the higher of any estimate.
Slightly more complex:
For a given potential delivery, if you plot the delivery times against the chances of them being happening, you're going to get a curve which is a variation of a normal distribution, and you can assume that a developers minimum estimates are going to be somewhere towards the left of the curve and their maximum towards the right.
The area under the curve to the left of the single number you select as your estimate represents the probability of you successfully delivering on or before that estimate. So if you give a number at the very left hand side your chance of hitting is effectively zero, if you give a number at the very right hand side your chance is effectively 100%.
What is less commonly realised is if you give the mean value (assuming your min and max averaged out give something approximating the actual mean) you'll only hit that deadline 50% of the time. Effectively if you use the mean you're going to miss the deadline half the time. I don't know about you but I don't like being seen as the guy whose misses half his deadlines.
So you want a number which is going to give you something you hit, say, 90% of the time. Conveniently 95% represents the mean + two standard deviations but if you can't be arsed to calculate that (and most of us probably don't have the data) my experience says that:
(3 x max + 1 x min) / 4
gives a reasonable result.
Incidentally, what you tell the developer is the deadline is another question entirely. Personally I'd give him somewhere around ((2 x max + 1 x min) / 3) and have the rest as contingency.
What are you using the estimates for? Specifically, why will the developer feel stressed if you normally underestimate?
If you're trying to schedule how long something is likely to take, you go for an intermediate value. Probably on the long side, since people normally underestimate. In any case, you shouldn't be using these estimates as firm objectives for developers, and so they shouldn't be overly stressful.
If you're using these estimates to set up commitments, you need to err on the side of overestimating. Giving developers insufficient time leads to burnout, unmaintainable buggy code that doesn't do quite what the user wants, and low morale and high turnover. Set the commitments to be reachable, and encourage the developers to finish early.
This depends on project.
Some projects may require fast development and there's no alternatives if deadline is already set and there's no good chance to prolong development. Typical issue: marketing campaign resulting in new service. Such deadline can be enough for normal development, but in some organizations it is so close, that developers work in stress and make many errors that are fixed during production stage. That's a kind of project when developers have to work with topmost effectiveness and they'd better get good reward on success.
Some projects are accurately planned and here you can use all analytics you have: history data, some developer's time metrics on subtasks, calculating risks, etc.
But anyway MAX time shouldn't be used: its the most inaccurate measure that usually leads to even more time taken. And here's a simple reason: when developer just gives away this MAX, he almost doesn't measure. He just gives away his intuition that has very little info at the time. But if he'll spend at least half an hour he'll understand specifics of his tasks, he even may split it into subtask and increase his accuracy. So you can give developer some bias like "hey, guys, just think in what time you would provide stable code here" but send him measure himself. It is good for a job, it is good for a programmer himself.
The first mistake most estimators use when setting the deadline is assuming that the dev will be full-time every day on that task which is a disastrous mistake. This can result in not meeting the deadline even when you use the over estimate to figure out the deadline. Being under the hours but past the deadline you told the client is a big problem. People take leave, get sick, have jury duty, have to go to required meetings on some new HR policy, get called over to help on another project when someone is stuck, have to load software on a new computer when their old one breaks, have to research a production problem on code they recently deployed, etc. If you are estimating more than 6 hours a day on the project per person, you are already in trouble on the deadline before the project starts. When I did manpower studies, we used a figure that equated to just slightly more than 6 hours a day of direct work when calculating out how many people were needed for any job. And we did a lot of statistical analysis as the basis for the figure we used.
I think you have to decide which of these to use on a case by case basis. We have some projects that we know the max estimate is still probably a little low (usually when someone in management couldn't face the client with the real estimate), we have others where we are doing something new where we know the estimates are more likely to be off, in these kinds of cases go with the max. But for work you've done before that is well-defined and you know the dev assigned won't be learning new skills, then go closer to the min (but never actually use the min, there are alawys unexpected bumps in the road). ALso the shorter the project, the more likely you will be able to meet the min, it is far easier to get a good estimate for a week-long project than a year-long one.
More importantly is changing the estimate and deadline every time the circumstances change. If the client adds work, the extend the deadline and estimate, don't just do it. If your best dev quits and you have to put someone new on the project, extend the deadline becasue that person has to have time to get up to speed (you may have to eat the hours though, the client may not agree to pay for that time. Critical to this is telling the client right away. They tend to be better about moving a deadline (although not happy) than they are about missing one or making the dealine but the product doesn't work as they expect it to. Too many project managers just like to wish a problem is going away and the won't have to face that conversation with the client. But usually when they do finally have to tell him it is a much worse conversation than the difficult one they tried to avoid.
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During our iteration planning, we frequently find ourselves in the same position as this guy - How to estimate a programming task if you have no experience in it
I definitely agree with prototyping before you can give a reasonable estimate. But the same applies to anything that needs a bit of architecture and design - but I'm not that comfortable doing all this outwith the scope of a sprint.
The basic idea is that you identify as many tasks as you can that you're confident of, and estimate these as normal. For those areas that you're unsure of then there should be two 'types' of task identified: Investigation & Implementation.
Investigation tasks are brief descriptions of work that you're just unsure of, for example "Investigate how to bind Control X to data". An estimate is provided for these.
The Implementation task is a traditional rough guess, probably based on the story points assigned, of how long you think it would take to implement the feature.
During the sprint, when the investigation tasks have been completed, the developer should then be at a stage where they have a much better idea what is going on. 'Proper' Tasks can then be identified, which take the place of the Implementation placeholder. In addition, further Investigation tasks may be identified at this stage, and the cycle continues.
In the above example, we start with an Investigation task at 7 hours and an Implementation task estimated at 14. Once the first Investigation has been completed, Tasks 1, 2 and 3 will be identified and estimated with some degree of certainty, where Task 3 is another Investigation task from which Task 4 and 5 will be identified at a later stage. As you can see, the first Implementation estimate had delivery of the feature within 14 hours - but the reality is it took at least 4 + 7 + 3 + 4 + 2 = 20. A third more than the initial estimate.
alt text http://www.duncangunn.me.uk/myweb/images/estimate.png
All thoughts are welcome - my gut instinct is this will fly - am I right or am I the Wrong Brothers?
Cheers!
What we do.
Some features involve new technology. We can't accurately estimate them. Period.
We make up a number. Based on a couple of things. How hard does it "feel"? Can we get by with some kind of "partial" or "just-enough" implementation?
If it's hard, then it's hard. It will be expensive.
If there's a lot of parts, with a kernel of goodness and some bonus stuff layered on, we have a possibility of putting just the kernel into a release, and setting other stuff aside for later. A very few things are "all or nothing" where a partial release isn't possible. In that case, we have to provide enough time for "all", and that gets expensive.
Our standard approach is to get stuff that works, and possibly defer things to a later sprint if we ran of out time because of unexpected complexities.
What you're calling "investigation", we call technical spike sprints. For stuff that's new, we make up estimate number to placate managers who feel it necessary to overplan things. Then we spike the technology. Once it's spiked, we can revise the estimates based on what we now know.
Actually, the implementation of the feature took 27 hours - you forgot the first investigation of 7 hours, so in reality the actual implementation took almost twice as long as the estimate.
There are two ways you can go on this:
Just make the estimate as best you can and potentially experience a blowout in your sprint and a declined project velocity (you should only do this if the feature is both urgent and critical); or
Schedule the investigation for this sprint and leave the implementation for another sprint - without an idea of how long the task will take, the Product Owner does not have enough information to make a decision about in which sprint to schedule it or even whether to do it at all. Only tasks that have been estimated should be included in your sprint.
The first choice means your sprint and project estimates are somewhat arbitrary. The second choice gives much more predictability to your sprints.
In your example, the initial investigation may be scheduled for Sprint 1 but without knowledge of how long the task will take the Product Owner can't decide how to schedule it. If you came back with an estimate of 200 hours the Product Owner may decide not to do that feature at all, or to delay it until Release 2 of the product. The estimate comes in and the Product Owner schedules Task 1, Task 2 and the investigation of Task 3 for Sprint 2. After estimating Task 3, Tasks 4 and 5 can be scheduled in Sprint 3 or later.
Estimating feature usually is complex task. After some time your estimation will become better. But good approach can be that you estimate features with the story points. Story point is abstract value (meaning agreed among the team) that express complexity of the problem.
You should assign the same complexity (same number of story points) to the features of the similar complexity. Then later on it is enough to estimate only smaller set of features (or looking at the historical data) and you should be able to estimate how much time you need.
Features with the similar complexity need similar time effort for implementation.