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As a lead developer I often get handed specifications for a new project, and get asked how long it'll take to complete the programming side of the work involved, in terms of hours.
I was just wondering how other developers calculate these times accurately?
Thanks!
Oh and I hope this isn't considered as a argumentitive question, I'm just interested in finding the best technique!
Estimation is often considered a black art, but it's actually much more manageable than people think.
At Inntec, we do contract software development, most if which involves working against a fixed cost. If our estimates were constantly way off, we would be out of business in no time.
But we've been in business for 15 years and we're profitable, so clearly this whole estimation thing is solvable.
Getting Started
Most people who insist that estimation is impossible are making wild guesses. That can work sporadically for the smallest projects, but it definitely does not scale. To get consistent accuracy, you need a systematic approach.
Years ago, my mentor told me what worked for him. It's a lot like Joel Spolsky's old estimation method, which you can read about here: Joel on Estimation. This is a simple, low-tech approach, and it works great for small teams. It may break down or require modification for larger teams, where communication and process overhead start to take up a significant percent of each developer's time.
In a nutshell, I use a spreadsheet to break the project down into small (less than 8 hour) chunks, taking into account everything from testing to communication to documentation. At the end I add in a 20% multiplier for unexpected items and bugs (which we have to fix for free for 30 days).
It is very hard to hold someone to an estimate that they had no part in devising. Some people like to have the whole team estimate each item and go with the highest number. I would say that at the very least, you should make pessimistic estimates and give your team a chance to speak up if they think you're off.
Learning and Improving
You need feedback to improve. That means tracking the actual hours you spend so that you can make a comparison and tune your estimation sense.
Right now at Inntec, before we start work on a big project, the spreadsheet line items become sticky notes on our kanban board, and the project manager tracks progress on them every day. Any time we go over or have an item we didn't consider, that goes up as a tiny red sticky, and it also goes into our burn-down report. Those two tools together provide invaluable feedback to the whole team.
Here's a pic of a typical kanban board, about halfway through a small project.
You might not be able to read the column headers, but they say Backlog, Brian, Keith, and Done. The backlog is broken down by groups (admin area, etc), and the developers have a column that shows the item(s) they're working on.
If you could look closely, all those notes have the estimated number of hours on them, and the ones in my column, if you were to add them up, should equal around 8, since that's how many hours are in my work day. It's unusual to have four in one day. Keith's column is empty, so he was probably out on this day.
If you have no idea what I'm talking about re: stand-up meetings, scrum, burn-down reports, etc then take a look at the scrum methodology. We don't follow it to the letter, but it has some great ideas not only for doing estimations, but for learning how to predict when your project will ship as new work is added and estimates are missed or met or exceeded (it does happen). You can look at this awesome tool called a burn-down report and say: we can indeed ship in one month, and let's look at our burn-down report to decide which features we're cutting.
FogBugz has something called Evidence-Based Scheduling which might be an easier, more automated way of getting the benefits I described above. Right now I am trying it out on a small project that starts in a few weeks. It has a built-in burn down report and it adapts to your scheduling inaccuracies, so that could be quite powerful.
Update: Just a quick note. A few years have passed, but so far I think everything in this post still holds up today. I updated it to use the word kanban, since the image above is actually a kanban board.
There is no general technique. You will have to rely on your (and your developers') experience. You will have to take into account all the environment and development process variables as well. And even if cope with this, there is a big chance you will miss something out.
I do not see any point in estimating the programming times only. The development process is so interconnected, that estimation of one side of it along won't produce any valuable result. The whole thing should be estimated, including programming, testing, deploying, developing architecture, writing doc (tech docs and user manual), creating and managing tickets in an issue tracker, meetings, vacations, sick leaves (sometime it is batter to wait for the guy, then assigning task to another one), planning sessions, coffee breaks.
Here is an example: it takes only 3 minutes for the egg to become roasted once you drop it on the frying pen. But if you say that it takes 3 minutes to make a fried egg, you are wrong. You missed out:
taking the frying pen (do you have one ready? Do you have to go and by one? Do you have to wait in the queue for this frying pen?)
making the fire (do you have an oven? will you have to get logs to build a fireplace?)
getting the oil (have any? have to buy some?)
getting an egg
frying it
serving it to the plate (have any ready? clean? wash it? buy it? wait for the dishwasher to finish?)
cleaning up after cooking (you won't the dirty frying pen, will you?)
Here is a good starting book on project estimation:
http://www.amazon.com/Software-Estimation-Demystifying-Practices-Microsoft/dp/0735605351
It has a good description on several estimation techniques. It get get you up in couple of hours of reading.
Good estimation comes with experience, or sometimes not at all.
At my current job, my 2 co-workers (that apparently had a lot more experience than me) usually underestimated times by 8 (yes, EIGHT) times. I, OTOH, have only once in the last 18 months gone over an original estimate.
Why does it happen? Neither of them, appeared to not actually know what they are doing, codewise, so they were literally thumb sucking.
Bottom line:
Never underestimate, it is much safer to overestimate. Given the latter you can always 'speed up' development, if needed. If you are already on a tight time-line, there is not much you can do.
If you're using FogBugz, then its Evidence Based Scheduling makes estimating a completion date very easy.
You may not be, but perhaps you could apply the principles of EBS to come up with an estimation.
This is probably one of the big misteries in the IT business. Countless failed software projects have shown that there is no perfect solution to this yet, but the closest thing to solving this I have found so far is to use the adaptive estimation mechanism built in to FogBugz.
Basically you are breaking your milestones into small tasks and guess how long it will take you to complete them. No task should be longer than about 8 hours. Then you enter all these tasks as planned features into Fogbugz. When completing the tasks, you track your time with FogBugz.
Fogbugz then evaluates your past estimates and actual time comsumption and uses that information to predict a window (with probabilities) in which you will have fulfilled your next few milestones.
Overestimation is rather better than underestimation. That's because we don't know the "unknown" and (in most cases) specifications do change during the software development lifecycle.
In my experience, we use iterative steps (just like in Agile Methodologies) to determine our timeline. We break down projects into components and overestimate on those components. The sum of these estimations used and add extra time for regression testing and deployment, and all the good work....
I think that you have to go back from your past projects, and learn from those mistakes to see how you can estimate wisely.
Let's say you have a project that will involve two web applications (that will share DAL/DAO/BO assemblies and some OSS libraries):
a semi complex management application that uses Windows Live ID for authentication and is also capable of communicating with various notifier services (email, sms, twitter etc.), targeted notifiers being about 10% of functionality
a low to semi complex user application with less functionality but more robustness that also uses Windows Live ID for authentication
There are two of us with medium estimation capabilities and we won't be able to do it in two days even if we wanted/have to. At least it would be a far off estimate.
Questions
How long would/does it normally take you to make a reliable/valuable estimate?
What would you suggest to speed-up estimation without sacrificing accuracy?
How much slack (in terms of cost/time) would you add depending on estimation speed (when you would say: I could estimate it a bit more, because I think it's still quite off)
Since we use Agile methods (Scrum, specifically) it takes us about an hour longer than it takes the users to prioritize.
More time doesn't lead to more accuracy.
The hard part, then, is getting the users to prioritize. We hear this discussion all the time "if the whole thing isn't completed on time, it's all worthless." "Except for the XYZZY component, which does have some value." That argument can go on for hours until it's resolved that XYZZY should be first.
Generally, we try to create 4-week sprints. The first few are complicated because there's always something new. After the first two (or three) we seem to set a steady pace.
Each use case has a relatively simple, subjective valuation of how the effort it will take to finish it. Anything over one full sprint in duration has to be decomposed. Most times a few use cases are bundled into a single sprint.
The are formal ways of scoring each use case to better handle the cost and schedule issues. We don't use them because the extra effort doesn't help.
After the first two sprints,
There's new and different functionality,
The priorities have all changed,
The details of each use case have been dramatically revised.
What does "accuracy" mean when the thing you're trying to estimate changes at the end of each sprint?
One lesson learned. Parts of my company spend a long time fully defining exactly what will be delivered, and then measuring that they are delivering precisely what they want.
Customers notice this, and one said we "spend a lot of time delivering what the contract says, but it isn't what we needed."
The problem with firm up-front estimates is that they take on a life of their own. The more you "invest" in the estimating, the more the estimates seem to be a useful deliverable. They aren't useful because they're generally totally wrong. They're based on up-front assumptions that are totally wrong.
It's a bad policy to invest more time in estimating. The "accurate" answers aren't more accurate, but they are more treasured by every layer of management. As you and the customer learn, you invalidate numerous assumptions and you absolutely must re-estimate constantly.
Don't do it up front. If your contract requires you to do it up front, then make sure you have a change control provision and tell the customer that you absolutely will make changes as you go forward. As both you and the customer learn, you both must make changes.
Interesting question. I'm afraid the answer is "it really depends!" I know that's not terribly useful (although it IS true) so here are some factors:
1) Quality and completeness of requirements and their specification. This is, to me, most often the project-estimate killer. If you don't have quality requirements, you have no reasonable basis for an estimate. We use a "RUP-lite" style of product development here, so as the engineering manager I won't give anything but the coarsest-grain estimate until we've completed our "elaboration" phase and gotten sign-off from product management that the core 80% of the product features are in fact accurately covered.
2) The scope & nature of the product. Bigger/more expensive/more complicated = substantially longer to estimate. I've spent years working in telco-carrier land delivering solutions which have the normal robust carrier requirements ("5 9's" of uptime required by SLA mean you must really do a good job of solution design and failure recovery!). In that sort of environment with all the moving parts across functional areas of the business, the estimation of work is going to hinge on getting the whole picture...specifically, cross-functional dependencies and external dependencies can be a REAL killer here. That said, I've also built lots of shrink-wrapped and enterprise software, too. In those environments it's much easier as the scope is typically substantially smaller, so thus easier to estimate.
3) How "new" is this project? How "new" is the team to this product or technology set? The newer the product or team, the longer and more buffer you should allocate.
4) How specific do we need to be? If this is a "rough guess" then I'll lean on my engineering leads to provide a conservative estimate, then I'll pad that. If we need a "real" estimate (e.g., one which is used by my boss and which I'll be responsible for hitting), I'd need the input from a number of different managers and team members, who will need time to analyze the requirements and confer amongst themselves.
That can take as little as a couple days, or weeks..it all depends on the size. "Two or three days" is, frankly, not long enough for sizing anything but the most trivial of projects.
The best thing you can do to improve the quality of your estimates to to improve the quality of your requirements, and be ruthless in identifying hidden dependencies.
One final thing: FWIW, I've been doing this since '81 and I regard accurately estimating a project's duration/cost as the single most difficult/fraught with peril part of engineering management.
To make a reliable estimate you really would need to create a list of tasks to be done. Break it down into stories/tasks (even if you don't use agile) and evaluate them. This can take a lot of time already - especially the amount of research (to look for libraries to do this notifier stuff to reduce the cost). I would take at least 3 days - however 1-2 week(s) sound more reasonable for me. This doesn't seem to be a small project.
I wouldn't dare to speed-up the estimation process if you wan't to have somewhat reasonable results. Estimations are never accurate and you will just make things worse.
An option would be to make a totally rough guess within a few hours and then start to work on it already (for a week or so). At the end of the week you might be able to give a better estimation based on your current progress. However it's important to create a good prototype (which looks like crap but has a little bit of code in all of the regions).
Well, a common quote is "The price will be between 50% and 400% of our initial estimates". The reason that this quote has grown large is that it's true. Estimation accuracy largely depends on your domain-knowledge. If this is the 100th time you sold a given type of blog-engine than you're pretty sure about the estimates. However, more often than not, you don't have that indepth knowledge of the domain (If the app already exists, why create a new one?).
Agile development has become popular because people largely recognize that the traditional "waterfall" type of thinking just doesn't work for most real-world projects. You should apply the same way of thinking to your estimates. Obviously, you need some kind of selling point, but be sure to tell your customers that this information is very vague (and that no matter what any competitor will tell them, their estimates are vague as well).
You need to sell iterations, and therefore also estimate iterations. I'm sure some marketing-guy in any company will know how to handle the paperwork and the legal stuff for this, so it shouldn't be that big a deal.
Consider a 5 iterations project:
Start out by putting up milestones. These are subject to change, but will provide your vargue first estimates for the final product.
Plan iteration #1.
Estimate iteration #1, adjust total estimates accordingly.
Do iteration #1
Rinse / repeat till iteration #5
You're done :)
Reflect over your project. How did your estimates evolve? Reasons? Learn by doing :)
Most customers would rather have part-estimates and part-deliverances than some unrealistic goal that some suit sold them :)
Straying slightly from your original question but it's also important to learn from the estimates that you have produced in the past by keeping information about how long it actually took to do the things that you estimated.
We estimated 5 days to produce these pages, it actually took 10 days, and etc.
In the long term information like this will (hopefully!) enable you to produce more accurate estimates.
Whilst answering “Dealing with awful estimates” posted by Ash I shared a few tips that I learned and personally use to spot weak estimates. But I am certain there must be many more!
What heuristics to use in the scenario when one needs to make a quick evaluation of software project estimate that has been compiled by a third-party (a colleague, a business partner or an external company)?
What are the obvious and not so obvious signs of weak software estimates that can be spotted without much detailed knowledge of task at hand?
A single person having done the estimates, rather than having used consensus based estimation (to fully understand the implied scope of requirements) such as Wideband Delphi.
Especially true if the person doing the estimation is not the person doing the implementation!! - I once worked on a project estimated by someone else as 60 days before any requirements had even been given. Lets just say I was not a happy bunny
No time for documentation.
No time for ramp-up (in terms of learning, and team size).
No list of risks, and their impact to the timescale.
No buffer for the unexpected - in terms of late breaking requirements, and risks.
No one has said it, so I will. The obvious answer is that if you have software schedule estimates then that is a sure sign of unrealistic figures. Yes, there are many methods for estimating software but none of them are accurate in any way, shape or form. What usually happens is that deadlines are set. If the task is over-estimated then extra time is spent making the result better. If the task is under-estimated then something is sacrificed to meet the delivery (like testing and features).
I know this answer isn’t what people want to believe, but estimating is always a guess. More often than not, a developer can’t even predict how much they will accomplish by the end of the day. You are expecting them to guess things months/years down the road on something that they aren’t even sure what is really involved yet.
The only practical answer to your question that isn’t prone to giving unrealistic results would be using a worksheet that comes up with guesses based on previous history at your company. Unfortunately, that will not account for tasks the estimator missed. At least this may give ballpark numbers.
Unless you develop knock offs of the same exact system over and over again, then anyone who thinks they have figured this out is fooling themselves. There are way too many variables involved.
There are two types of estimates: task estimates and project estimates. You can view these as the big and small pictures.
Project estimates are necessarily high level (granularity no smaller than days typically) and must include things like:
High level architecture;
Time for testing;
Ramp up times;
Defect processes;
Time for documentation;
Relevant training;
Assumptions;
Dependencies (eg team A can't do what they need to until team B delivers phase 1);
Critical path (which pieces are likely to determine if the project slips and by how much); and
Risks.
The more of those things that are missing, the more unrealistic (or risky) the estimates.
The second kind of a task estimate, which is typically much lower level. For this kind of estimate it should be simply a task breakdown (with no task being larger than say 5 days).
These don't tend to address the above items but some of them might be relevant, such as assumptions regarding decisions not made yet (eg production hardware). It may also be worth identifying who can and can't do the tasks due to relevant experience, background knowledge or skills (as that person or those persons may end up overcommitted).
Other posts have mentioned the testing time should equal or exceed dev time. I strongly disagree with this. I've seen 8 hour dev tasks result in 100+ hours test time and 80 hour dev tasks result in less than 2 hours of testing. In both cases the testing time was entirely reasonable. The is no absolute correlation between the two. At best, there is a loose connection.
Is the estimate what the management
wanted to be told?
Does the
estimate nicely fit in with the
planned shipment date for the next
release?
Does the management reward
people that give good news more then
people the give bad news?
Was the
estimate prepared before knowing who
would be working on the project?
Did
someone that wanted that bit of
functionally implemented prepare the
estimate?
Is there a history of
software being late?
Is it normal for
developers to be moved onto other
tasks partway though a project?
Have
some or all developers given up on
commenting on bad estimates as a
waste of time?
Count up the number of questions you get “yes” or “maybe” answers.…
If you get mostly “no” answers to the above questions, then it may be worth looking at the estimate in detail to see if it includes the tasks that other people of listed in this thread.
Wow... I really like toolkit's answer.
And I agree that any estimate at all is flawed, because it assumes that the estimator has way more of a clue for how to solve the problem than any estimator actually does when a project gets estimated. However, I think you still need to at least estimate the size of the mountain before you start. Some thought as to whether it's worth trying to do it should precede any endeavor and that's what the essence of an estimate should be.
I did come up with a few more indicators of a dangerous estimate:
No cross-reference - If the estimate can't be validated at least two different ways, it's likely to be unreliable. For example, the last estimates I've done I've been able to break down the work into small feature chunks, and consider how long it's taken our team to do similarly scoped features. Then I was able to look at the sum of these costs and see how big the scope was relative to other projects I've worked on. That's two ways to validate.
The background of the estimator - if this is a software estimate done by a hardware guy who's never written code - be very afraid. More subtle - the closer the estimator's background is to the technology and problem domain of the project, the better.
Detail - as said a few different ways in a few different posts - I like to see detail for both individual features, as well as the tasks needed to complete each feature. Most estimates I see don't show the detail in the formal presentation, but if you ask the person who did the estimate, they should have a file somewhere. Hopefully it's not the back of a paper napkin stained with beer and ketchup. :)
Documented Assumptions - any estimator will have had to make some set of assumptions about the task. These should be documented somewhere, perferably in the formal paperwork. I always get a little worried when I see a short proposal with not many documented assumptions. Either they were thought through and not communicated to the customer, or they were not thought through. I'm not honestly sure which one is worse. It goes without saying that the assumptions should also be reasonable.
Balanced Lifecycle - However the task is broken down, what's the ratio of design, code and test? How about documentation, integration with external systems and post release support? How about those extra things that are so vital (system admins, CM gurus, management effort)?
Slack - I'm sure the corporate daemons of cheapness will come and flay me, but a schedule and a cost should have some slack. If the estimate looks ambitious and agressive to an experienced eye, it is likely to be too low. Estimates are almost never too high.
One good heuristic is to see if test time is roughly the same a development time. That's a good sign for the estimate.
If they can't give you a breakdown of the estimate then that's a bad thing. Usually a sign of lots of little things that may have been forgotten. They don't need to provide the complete original breakdown, just a breakdown like:
requirements
development
testing
packaging and deployment
etc.
They should be using a standard template to calculate their estimate. They don't need a number in every column, but they do the template to list all possible tasks. That way the template can be used to jog peoples's minds when working on the estimate.
If the estimate is overly precise, e.g. 0.25 hour increments, then that, for me, is a bad smell.
If there are things missing like requirements capture, testing, deployment, and handover to any Ops group? If any of those are missing, that's the sort of thing that will come back and bite you.
Edit: One other thing to watch for is the old "perpetually 90% complete" tasks. You get progress update after progress update listing a task as "90% complete". That's not good!
HTH
cheers
Is the compiler of the
estimate available and willing to
discuss it with other senior project
members? If not, that is often a
concern.
Was the estimate sent to the
customer before the experience and
skills of the development staff are
known. Two point estimates may help
but only to some extent.
Before even getting a chance to look at the estimate, you are told that you are committed to delivering all of the functionality described by a specific date.
(Thanks for responding to my question, by the way.)
If you see one or more of these, you may have a bad estimate:
Single point estimates: an estimate should be associated with a range of possible dates or a confidence value
Insufficient granularity of tasks: a large task bucket usually indicates that the functionality is not well understood (which is especially a problem since poorly understood problems are usually under-estimated)
No expression of assumptions and/or risks
Inadequate effort allocated for commonly skipped or underestimated items (e.g. build scripts, documentation, deployment, etc.)
I agree with sateesh, I really like Software Estimation: Demystifying the Black Art by Steve McConnell. He has several checklists which are useful when reviewing and/or preparing estimates.
I totally agree with Dunk, the first sign of bad estimates is the mere presence of a large detailed upfront schedule. Estimates are exactly that, an approximation, otherwise we would call them exactimates. So they should never be used alone in the management of a project.
The most important point to consider is not the accuracy of estimates but the consistency. If a third party were doing estimates for you, then ask to see a history of their successes or failures, speak with their past clients and determine their reliability.
That all being said, from an Agile standpoint, some of the ways we attempt to gain more consistent estimates during a project are;
Use a relative sizing standard (S,M,L,XL) rather than time based (ideal days).
focus on complexity not time
Always use group estimates not single person estimates
Gather estimates frequently throughout the project, generally at the start of each sprint
use feedback from previous sprints in determination of story complexity
track velocity to give meaning to the relative sizing
frequent and early story retrospection to examine/understand any thrashing
If you are dealing with companies that use these estimation methods then, chances are you are going to receive consistent and therefore better results.
Estimates of the form 3, 6, or 12 months (basically any round numbers) reek of guessing. Usually when you guess you pick some round number bigger than you think it will take -- quarters, half a year, etc. -- are the usual suspects. I much prefer estimates in terms of actual development iterations (whatever their size).
What are the obvious and not so
obvious signs of weak software
estimates that can be spotted without
much detailed knowledge of task at
hand?
Estimates which are given without much detailed knowledge of the task at hand are generally not good.
Perhaps a general approach you could take is to check that items in the requirements correspond to those in the estimate. If you want to be very quick check the relative sizes, if there is a 100 word estimate given to a 100,000 word brief it stands no chance of being right.
Also (as others have said) check that analysis, coding, debugging, testing, integration, contingency etc are mentioned. It shows some thought has gone into it.
Having success and sign off criteria at various stages is a great sign. If they have a defined point which is 10% done at least if the estimate is wrong you know early and have a chance to adapt. If there are no checkpoints until “finish” you may not know that you are behind until that date is hit.
How familar is the person giving the estimate with the people doing the work?
I have often seen estimates where there is a generic person doing the work, even though the team is made up of individuals with very different backgrounds. Most likely the tasks and the specialities don't line up perfectly and you get a c++ serverside programmer who ends up doing either your gui or your database... Sometimes the manager of the team doesn't really appreciate the team member's strengths, so if he has been asked to come up with the estimate on his own because his team is busy on the previous project you will find that the work in question is really only suitable for part of the team (not motivating, lack of skills etc)
One other helpful way to evaluate the estimates is to compare it with the actual effort that was spent on previous projects of similar kind. The best data for the comparison would be the effort data of the previous projects that the organization has done. If there is no organizational historical data you can try to benchmark the estimate against industry wide benchmarks.
I would also say if the estimate is presented as single absolute number (say 180 days) then it is not a good sign. A single absolute number would indicate that the estimate is that the task will be finished with 100% probability on the given data. The estimates presented in a range (say 130 to 180 days) would indicate that the range in which task could be completed.
Much of what I have written above I attribute it to the book :
Software Estimation: Demystifying the Black Art
by Steve McConnell
I check the estimates against the man-power. Although not a very accurate heuristic, it's clear if some massive work has just one or two devs assigned to it, that the task was not estimated correctly
A good estimate will have a good breakdown, involving all phases of the project.
It will almost certainly not finish at a convenient date for the business.
It will include risks of various sorts.
It will be presented in terms of confidence intervals, either implicitly (10-12 months) or by using large units (about four quarters).
It will be made by somebody with responsibility for getting the project done, preferably more than one such person.
If there are delays at the start, there will be delays at the end (expressed as 10-12 months from start, or about 1Q2010 if we start now, not something like January 2010 when the project hasn't started yet).
Assumptions and dependencies will be clearly and prominently listed.
Edit: Part of this depends on the stage the project is in. An early but precise estimate is a warning sign, particularly if there is no confidence interval assigned. That reeks of a Procrustean estimate.
Also, watch for other development methodologies. A timeboxed project can have a rigid and arbitrary schedule, but the feature set will be flexible.
Any of the following:
It is one big project and there isn't a short high level strategy described
There isn't a clear, short and concise vision of what wants to be achieve with the project
The project isn't structured around business value being delivered gradually
The team is trying to give "accurate" estimates for a big project, going into (or was done with) a long analysis phase? (changes will come, and will usually affect those estimates in way that can't be easily quantified without yet more big efforts)
There are "detailed" estimates for the whole project (related to previous)
There aren't detailed estimates for the first phase, or there is something wrong in those.
"Four to six weeks", when not accompanied with a breakdown into shorter tasks...
I am using FogBugz as a tool to give us "a look into the future". The program takes our work hours, the tasks for a release, assigned developer's estimate against that task, and the developers tendency to under/over estimate, and tries to come up with a probability of making the release against a range of dates in the future.
Now since FogBugz takes into account the work hours, it assumes that the developers will put in the hours into the tasks they are assigned, which is not true in XP because the earlier decision of the pair was to work on one of the developers' tasks together.
Does this mean that I cant use FogBugz for estimates when doing pair programming?
What I would do in this case would be to have each developer estimate each of his cases for the release expressed in working hours it will take to complete it while pair programming (that is, time spent with a partner actually working on that case). Then figure out about how much time you spend pair programming on your own tasks vs. on someone else's tasks, and set your "% time spent on FogBugz tasks" on your Working Schedule to the approximate percentage of time you spend on your own tasks.
Then, when you're working on your tasks, mark yourself as Working On -> case ID, and when you're working on someone else's tasks, set Working On -> Nothing.
There are actually a lot of different ways to get get this to work (and the other answers here might be better depending on the circumstances), but that's how I would do it.
If the two programmers are working together, then for all practical purposes they are like one programmer with two heads, no? Why not create another user account in FogBugz that represents them both? They should then also produce their estimates together. That might actually increase precision.
Does this mean that I cant use FogBugz for estimates when doing pair programming?
I don't have any FogBogz experience but I'd say “give it a try”. The whole point of the time estimates in FogBogz is that the software learns from experience and automatically corrects estimates based on this. This is an incredibly strong mechanism because in practice, most people's estimates are worth squat. It'd be interesting to see if FogBugs can also cope with pair estimates. I expect the error margin to be quite a bit higher but perhaps the estimtates are still usable.
Does this mean that I cant use FogBugz for estimates when doing pair programming?
I don't have experience with it, but intuition would tell me "it depends"
To elaborate - Say you have 2 programmers, John and Bob, both with FB accounts:
Does John always Pair-Program with Bob?
- John's estimates should be consistent with respect to his actual completion times. Even if he does his estimates based on what he alone thinks, the 'velocity' calculations should make up for this
Does John sometimes Pair-Program with Bob, and sometimes by himself?
- Provided John knows ahead of time which projects will be paired, and which won't, he will adjust his estimates accordingly. They might still be wrong, but the velocity calculations should probably still be ok? maybe?
Does John pair-program with a wide variety of partners? (optionally including programming alone)
- You're screwed. There are too many variables in motion for John to be able to produce any kind of useful estimates, let alone for FB or anything (or anyone) else to compensate for them.
I came across this article about programming styles, seen by Edsger Dijsktra. To quickly paraphrase, the main difference is Mozart, when the analogy is made to programming, fully understood (debatable) the problem before writing anything, while Beethoven made his decisions as he wrote the notes out on paper, creating many revisions along the way. With Mozart programming, version 1.0 would be the only version for software that should aim to work with no errors and maximum efficiency. Also, Dijkstra says software not at that level of refinement and stability should not be released to the public.
Based on his views, two questions. Is Mozart programming even possible? Would the software we write today really benefit if we adopted the Mozart style instead?
My thoughts. It seems, to address the increasing complexity of software, we've moved on from this method to things like agile development, public beta testing, and constant revisions, methods that define web development, where speed matters most. But when I think of all the revisions web software can go through, especially during maintenance, when often patches are applied over patches, to then be refined through a tedious refactoring process—the Mozart way seems very attractive. It would at least lessen those annoying software updates, e.g. Digsby, Windows, iTunes, etc., many the result of unforeseen vulnerabilities that require a new and immediate release.
Edit: Refer to the response below for a more accurate explanation of Dijsktra's views.
The Mozart programming style is a complete myth (everybody has to edit and modify their initial efforts), and although "Mozart" is essentially a metaphor in this example, it's worth noting that Mozart was substantially a myth himself.
Mozart was a supposed magical child prodigy who composed his first sonata at 4 (he was actually 6, and it sucked - you won't ever hear it performed anywhere). It's rarely mentioned, of course, that his father was considered Europe's greatest music teacher, and that he forced all of his children to practice playing and composition for hours each day as soon as they could pick up an instrument or a pen.
Mozart himself was careful to perpetuate the illusion that his music emerged whole from his mind by destroying most of his drafts, although enough survive to show that he was an editor like everyone else. Beethoven was just more honest about the process (maybe because he was deaf and couldn't tell if anyone was sneaking up on him anyway).
I won't even mention the theory that Mozart got his melodies from listening to songbirds. Or the fact that he created a system that used dice to randomly generate music (which is actually pretty cool, but might also explain how much of Mozart's music appeared to come from nowhere).
The moral of the story is: don't believe the hype. Programming is work, followed by more work to fix the mistakes you made the first time around, followed by more work to fix the mistakes you made the second time around, and so on and so forth until you die.
It doesn't scale.
I can figure out a line of code in my head, a routine, and even a small program. But a medium program? There are probably some guys that can do it, but how many, and how much do they cost? And should they really write the next payroll program? That's like wasting Mozart on muzak.
Now, try to imagine a team of Mozarts. Just for a few seconds.
Still it is a powerful instrument. If you can figure out a whole line in your head, do it. If you can figure out a small routine with all its funny cases, do it.
On the surface, it avoids going back to the drawing board because you didn't think of one edge case that requires a completely different interface altogether.
The deeper meaning (head fake?) can be explained by learning another human language. For a long time you thinking which words represent your thoughts, and how to order them into a valid sentence - that transcription costs a lot of foreground cycles.
One day you will notice the liberating feeling that you just talk. It may feel like "thinking in a foregin language", or as if "the words come naturally". You will sometimes stumble, looking for a particular word or idiom, but most of the time translation runs in the vast ressources of the "subconcious CPU".
The "high goal" is developing a mental model of the solution that is (mostly) independent of the implementation language, to separate solution of a problem from transcribing the problem. Transcription is easy, repetetive and easily trained, and abstract solutions can be reused.
I have no idea how this could be taught, but "figuring out as much as possible before you start to write it" sounds like good programming practice towards that goal.
A classic story from Usenet, about a true programming Mozart.
Real Programmers write in Fortran.
Maybe they do now, in this decadent
era of Lite beer, hand calculators and
"user-friendly" software but back in
the Good Old Days, when the term
"software" sounded funny and Real
Computers were made out of drums and
vacuum tubes, Real Programmers wrote
in machine code. Not Fortran. Not
RATFOR. Not, even, assembly language.
Machine Code. Raw, unadorned,
inscrutable hexadecimal numbers.
Directly.
Lest a whole new generation of
programmers grow up in ignorance of
this glorious past, I feel duty-bound
to describe, as best I can through the
generation gap, how a Real Programmer
wrote code. I'll call him Mel, because
that was his name.
I first met Mel when I went to work
for Royal McBee Computer Corp., a
now-defunct subsidiary of the
typewriter company. The firm
manufactured the LGP-30, a small,
cheap (by the standards of the day)
drum-memory computer, and had just
started to manufacture the RPC-4000, a
much-improved, bigger, better, faster
-- drum-memory computer. Cores cost too much, and weren't here to stay,
anyway. (That's why you haven't heard
of the company, or the computer.)
I had been hired to write a Fortran
compiler for this new marvel and Mel
was my guide to its wonders. Mel
didn't approve of compilers.
"If a program can't rewrite its own
code," he asked, "what good is it?"
Mel had written, in hexadecimal, the
most popular computer program the
company owned. It ran on the LGP-30
and played blackjack with potential
customers at computer shows. Its
effect was always dramatic. The LGP-30
booth was packed at every show, and
the IBM salesmen stood around talking
to each other. Whether or not this
actually sold computers was a question
we never discussed.
Mel's job was to re-write the
blackjack program for the RPC-4000.
(Port? What does that mean?) The new
computer had a one-plus-one addressing
scheme, in which each machine
instruction, in addition to the
operation code and the address of the
needed operand, had a second address
that indicated where, on the revolving
drum, the next instruction was
located. In modern parlance, every
single instruction was followed by a
GO TO! Put that in Pascal's pipe and
smoke it.
Mel loved the RPC-4000 because he
could optimize his code: that is,
locate instructions on the drum so
that just as one finished its job, the
next would be just arriving at the
"read head" and available for
immediate execution. There was a
program to do that job, an "optimizing
assembler", but Mel refused to use it.
"You never know where it's going to
put things", he explained, "so you'd
have to use separate constants".
It was a long time before I understood
that remark. Since Mel knew the
numerical value of every operation
code, and assigned his own drum
addresses, every instruction he wrote
could also be considered a numerical
constant. He could pick up an earlier
"add" instruction, say, and multiply
by it, if it had the right numeric
value. His code was not easy for
someone else to modify.
I compared Mel's hand-optimized
programs with the same code massaged
by the optimizing assembler program,
and Mel's always ran faster. That was
because the "top-down" method of
program design hadn't been invented
yet, and Mel wouldn't have used it
anyway. He wrote the innermost parts
of his program loops first, so they
would get first choice of the optimum
address locations on the drum. The
optimizing assembler wasn't smart
enough to do it that way.
Mel never wrote time-delay loops,
either, even when the balky
Flexowriter required a delay between
output characters to work right. He
just located instructions on the drum
so each successive one was just past
the read head when it was needed; the
drum had to execute another complete
revolution to find the next
instruction. He coined an
unforgettable term for this procedure.
Although "optimum" is an absolute
term, like "unique", it became common
verbal practice to make it relative:
"not quite optimum" or "less optimum"
or "not very optimum". Mel called the
maximum time-delay locations the "most
pessimum".
After he finished the blackjack
program and got it to run, ("Even the
initializer is optimized", he said
proudly) he got a Change Request from
the sales department. The program used
an elegant (optimized) random number
generator to shuffle the "cards" and
deal from the "deck", and some of the
salesmen felt it was too fair, since
sometimes the customers lost. They
wanted Mel to modify the program so,
at the setting of a sense switch on
the console, they could change the
odds and let the customer win.
Mel balked. He felt this was patently
dishonest, which it was, and that it
impinged on his personal integrity as
a programmer, which it did, so he
refused to do it. The Head Salesman
talked to Mel, as did the Big Boss
and, at the boss's urging, a few
Fellow Programmers. Mel finally gave
in and wrote the code, but he got the
test backwards, and, when the sense
switch was turned on, the program
would cheat, winning every time. Mel
was delighted with this, claiming his
subconscious was uncontrollably
ethical, and adamantly refused to fix
it.
After Mel had left the company for
greener pa$ture$, the Big Boss asked
me to look at the code and see if I
could find the test and reverse it.
Somewhat reluctantly, I agreed to
look. Tracking Mel's code was a real
adventure.
I have often felt that programming is
an art form, whose real value can only
be appreciated by another versed in
the same arcane art; there are lovely
gems and brilliant coups hidden from
human view and admiration, sometimes
forever, by the very nature of the
process. You can learn a lot about an
individual just by reading through his
code, even in hexadecimal. Mel was, I
think, an unsung genius.
Perhaps my greatest shock came when I
found an innocent loop that had no
test in it. No test. None. Common
sense said it had to be a closed loop,
where the program would circle,
forever, endlessly. Program control
passed right through it, however, and
safely out the other side. It took me
two weeks to figure it out.
The RPC-4000 computer had a really
modern facility called an index
register. It allowed the programmer to
write a program loop that used an
indexed instruction inside; each time
through, the number in the index
register was added to the address of
that instruction, so it would refer to
the next datum in a series. He had
only to increment the index register
each time through. Mel never used it.
Instead, he would pull the instruction
into a machine register, add one to
its address, and store it back. He
would then execute the modified
instruction right from the register.
The loop was written so this
additional execution time was taken
into account -- just as this
instruction finished, the next one was
right under the drum's read head,
ready to go. But the loop had no test
in it.
The vital clue came when I noticed the
index register bit, the bit that lay
between the address and the operation
code in the instruction word, was
turned on-- yet Mel never used the
index register, leaving it zero all
the time. When the light went on it
nearly blinded me.
He had located the data he was working
on near the top of memory -- the
largest locations the instructions
could address -- so, after the last
datum was handled, incrementing the
instruction address would make it
overflow. The carry would add one to
the operation code, changing it to the
next one in the instruction set: a
jump instruction. Sure enough, the
next program instruction was in
address location zero, and the program
went happily on its way.
I haven't kept in touch with Mel, so I
don't know if he ever gave in to the
flood of change that has washed over
programming techniques since those
long-gone days. I like to think he
didn't. In any event, I was impressed
enough that I quit looking for the
offending test, telling the Big Boss I
couldn't find it. He didn't seem
surprised.
When I left the company, the blackjack
program would still cheat if you
turned on the right sense switch, and
I think that's how it should be. I
didn't feel comfortable hacking up the
code of a Real Programmer.
Edsger Dijkstra discusses his views on Mozart vs Beethoven programming in this YouTube video entitled "Discipline in Thought".
People in this thread have pretty much discussed how Dikstra's views are impractical. I'm going to try and defend him some.
Dijkstra is against companies
essentially "testing" their software
on their customers. Releasing
version 1.0 and then immediately
patch 1.1. He felt that the program
should be polished to a degree that
"hotfix" patches are borderline
unethical.
He did not think that software should be written in one fell swoop or that changes would never need to be made. He often discusses his design ideals, one of them being modularity and ease of change. He often thought that individual algorithms should be written in this way however, after you have completely understood the problem. That was part of his discipline.
He found after all his extensive experience with programmers, that programmers aren't happy unless they are pushing the limits of their knowledge. He said that programmers didn't want to program something they completely and 100% understood because there was no challenge in it. Programmers always wanted to be on the brink of their knowledge. While he understood why programmers are like that he stated that it wasn't representative of low-error tolerance programming.
There are some industries or applications of programming that I believe Dijkstra's "discipline" are warranted as well. NASA Rovers, Health Industry embedded devices (ie dispense medication, etc), certain Financial software that transfer our money. These areas don't have the luxuries of incremental change after release and a more "Mozart Approach" is necessary.
I think the Mozart story confuses what gets shipped versus how it is developed. Beethoven did not beta-test his symphonies on the public. (It would be interesting to see how much he changed any of the scores after the first public performance.)
I also don't think that Dijkstra was insisting that it all be done in your head. After all, he wrote books on disciplined programming that involved working it out on paper, and to the same extent that he wanted to see mathematical-quality discipline, have you noticed how much paper and chalk board mathematicians may consume while working on a problem?
I favor Simucal's response, but I think the Mozart-Beethoven metaphor should be discarded. That shoe-horns Dijkstra's insistence on discipline and understanding into a corner where it really doesn't belong.
Additional Remarks:
The TV popularization is not so hot, and it confuses some things about musical composition and what a composer is doing and what a programmer is doing. In Dijkstra's own words, from his 1972 Turing Award Lecture: "We must not forget that it is not our business to make programs; it is our business to design classes of computations that will display a desired behavior." A composer may be out to discover the desired behavior.
Also, in Dijkstra's notion that version 1.0 should be the final version, we too easily confuse how desired behavior and functionality evolve over time. I believe he oversimplifies in thinking that all future versions are because the first one was not thought out and done rigorously and reliably.
Even without time-to-market urgency, I think we now understand much better that important kinds of software evolve along with the users experience with it and the utilitarian purpose they have for it. Obvious counter-examples are games (also consider how theatrical motion pictures are developed). Do you think Beethoven could have written Symphony No. 9 without all of his preceding experience and exploration? Do you think the audience could have heard it for what it was? Should he have waited until he had the perfect Sonata? I'm sure Dijkstra doesn't propose this, but I do think he goes too far with Mozart-Beethoven to make his point.
In addition, consider chess-playing software. The new versions are not because the previous ones didn't play correctly. It is about exploiting advances in chess-playing heuristics and the available computer power. For this and many other situations, the idea that version 1.0 be the final version is off base. I understand that he is rightfully objecting to the release of known-to-be unreliable and maybe impaired software with deficiencies to be made up in maintenance and future releases. But the Mozartian counter-argument doesn't hold up for me.
So, did Dijkstra continue to drive the first automobile he purchased, or clones of exactly that automobile? Maybe there is planned obsolescence, but a lot of it has to do with improvements and reliability that could not have possibly been available or even considered in previous generations of automotive technology.
I am a big Dijkstra fan, but I think the Mozart-Beethoven thing is way too simplistic as well as inappropriate. I am a big Beethoven fan too.
I think it's possible to appear to employ Mozart programming. I know of one company, Blizzard, that doesn't release a software product until they're good and ready. This doesn't mean that Diablo 3 will spring whole and complete from someone's mind in one session of dazzlingly brilliant coding. It does mean that that's how it will appear to the rest of us. Blizzard will test the heck out of their game internally, not showing it to the rest of the world until they've got all the kinks worked out. Most companies don't take this approach, preferring instead to release software when it's good enough to solve a problem, then fix bugs and add features as they come up. This approach works (to varying degrees) for most companies.
Well, we can't all be as good as Mozart, can we? Perhaps Beethoven programming is easier.
If Apple adopted "Mozart" programming, there would be no Mac OS X or iTunes today.
If Google adopted "Mozart" programming, there would be no Gmail or Google Reader.
If SO developers adopted "Mozart" programming, there would be no SO today.
If Microsoft adopted "Mozart" programming, there would be no Windows today (well, I think that would be good).
So the answer is simply NO. Nothing is perfect, and nothing is ever meant to be perfect, and that includes software.
I think the idea is to plan ahead. You need to at least have some kind of outline of what you are trying to do and how you plan to get there. If you just sit down at the keyboard and hope "the muse" will lead you to where your program needs to go, the results are liable to be rather uneven, and it will take you much longer to get there.
This is true with any kind of writing. Very few authors just sit down at a typewriter with no ideas and start banging away until a bestselling novel is produced. Heck, my father-in-law (a high school English teacher) actually writes outlines for his letters.
Progress in computing is worth a sacrifice in glory or genius here and there.