One Team’s First Sprint Planning Session

This post is a bit different than my usual ones. Instead of orating off of my soapbox, I’d like to share a moment of agile bliss that I had today. One of the teams I work with at my customer’s company conducted their first “truly agile” sprint planning session. This post is a recount of the experience.

What Came Before

About a month ago, I began working on agile methodology training for a few teams at my client. The focus of the training was team work-process, mostly using Scrum as a template for how we will do things, with a few Kanban inspired overrides. In my first few sessions, I focused on what a user story is, what a task is, and the concept of focusing the team’s energy on completing user stories as fast as possible.

What I taught them boiled down to a few key take-away points:

• A user story is an independent increment in functionality that is valuable to a stakeholder, that can be completed by the team in one sprint
• A task is one developer’s day of work, on one component of the system that is affected by its user story, or work that needs to be done to meet the team’s quality standards.
• Each team member pulls an unassigned task from the most important, unfinished user story

Initially there were arguments on all points: “Our stories are too big for one sprint”, “What if a task takes more or less than a day?”, “It’s easier to assign a story to a developer”. I had to convince them that these basic ideas are important and valuable, or at least to give them a try.

The Man’s Too Big…

What we did was take one “too big” user story, and look for some way to break them down in to thinner vertical slices. We ended up with stories that use naïve implementations, or serve a smaller set of clients, or handle a smaller set of scenarios, with further stories that cover the difference in robustness, clients and scenarios.

With regard to the tasks, I suggested that we simply split tasks that feel too big, or join tasks that feel to small, and not to worry about their actual length. We’ll measure the averages and variance after the end of the sprint, and look for better ways to be more predictable as needed. It was more of a let’s simply try it solution.

The Man’s Too Strong!

The longest argument was over whether the first story warranted having multiple developers swarm on the tasks until the story is complete. One developer felt most strongly that it’s a waste of time to have multiple people learn the story, since we’re certain to complete it in time either way. Another developer didn’t want to be pegged in just the server (or client) coding tasks of each story, and felt that owning a whole story gave her more satisfaction. She also expressed concern over the pain of complex merges. Incidentally, it was mostly the QA engineers that immediately accepted the value of working on one story’s tasks in parallel.

Here are the points I made to alleviate their concerns:

• Having multiple developers learn a story is a great benefit, as it will allow flexibility in task assignment now, and further down the line in future sprints that may have related stories
• Having more people understand the story will eliminate bottlenecks (and increases the team’s bus factor – a good thing)
• Regarding the perceived focusing on one component, some other team member chimed in, suggesting that nothing would stop her from taking a client-side task in one story, and a server-related one in the next. I was really happy that other members joined the discussion; it was no longer a teacher-class relationship, but a team of peers
• I also reminded the team, that since we define a task as the work on one component for a day, and since we focus on completing one story before moving to the next, we are actually reducing the chance of two developers having to manipulate the same object at the same time – which of course reduces the merge conflicts! Further effort can be made to remove the rest of the edge cases almost entirely
• I further mentioned that there will be at least two members working on each story in any case: one coder, one tester.

Planning the Sprint

The rest went by rather smoothly. The team calculated how many expected work days they have in the sprint. I suggested that we simply add them up, rather than trying to split along the lines of coder / tester days & tasks.

I noticed that the backlog had some free-floating tasks, i.e. not related to any user story. I asked the team about those, stating that unless there is some value to a stakeholder, the task may be a waste of time. The team told me that it was technical debt. After some discussion, we agreed to deduct the tasks from the available work days, rather than creating fake user stories – we have to own up to the fact that we are paying back a debt to the system, not adding value to it.

The team-leader-turned-part-time-product-owner gave a rough stack ranking (there were several items high on the list with the same rank). I suggested that if he doesn’t care which of the same-rank stories we complete first, I’ll set an internal rank by the order that they appeared in the spreadsheet. He accepted.

Next we started explaining and breaking the stories. Traditionally, the PO explains the stories in the planning session’s first half, and the team breaks them down in the second. Since the PO in this case is a team member, and fully savvy of the product and solution, I suggested that we simply run down the list until we have as many tasks as we have work days (see how that works out?).

The first story was easy. We created 4 coding tasks and 2 quality related tasks.

Break it Down!

The second story was a whopper. We knew it would take most of the sprint. One developer suggested that we split the story by data source – one part retrieving one source’s data, the other part retrieving the second source. the team leader shook his head. “The customer needs both”, he said. There is no value in supplying just one. We’ll split it some other way, if we have to”.

You couldn’t erase the smile from my face with truckload of erasers. I knew for sure that he got the idea of what a user story is!

With the second, larger, story, the team leader frowned and looked at the task list. “This doesn’t feel right” he said. When asked what the problem was, he said that one task is too big – it will take longer than a day. The team then split it in two parts, and added a third task.

And we were done estimating and committing. Just like that.

All in all, we spent about an hour of planning, 2-3 minutes of which were spent “estimating”, which is to say deciding how to split that “too big” task.

An Agile Manager in a Brave New World

At the end of the meeting, the team leader expressed his regrets that we don’t have a full-time technical-product-manager (TPM) which is the closest role they have to a Product Owner. The group manager, who was present for the Sprint Planning picked up the impediment and answered “Let’s take it offline; I’ll try to get one to join the team ASAP”.

Yes sir! The manager became the Scrum Master’s Scrum Master, as per my suggestion in my previous post.

Here’s What We Didn’t Do

There are several things the team didn’t do:

• Defining proper user stories – I didn’t bother following the ceremony of form with the stories, declaring the reasoning and the interested party. I ignored this because I wanted to avoid too much change-shock at once. Next sprint we’ll focus on that. For now, I felt that understanding the concept of value was more – well – valuable
• Estimate the effort involved in the stories. Personally? I hate estimations. I think they’re useless, most of the time. I noticed that most of the time spent in a sprint planning session is wasted on planning poker, story points and ideal hours, and in the end – they don’t help the project’s predictability in any noticeable way. Instead, we’ll measure, and seek a low-variance way to split the tasks. That will be better. and cheaper

In the End

All in all, it was a great first “agile day” for the team. It was fast, to the point, and everybody participated in every part of the session. Tomorrow I will work with them on building their Scrum-board, and walk them through their first “real” daily meeting. I can’t wait!

So, what do you think? Want to hear more such experiences? Is there anything I did that you like? Something I could have done better? Please drop a comment. Don’t be a stranger!

P.S. A special thank you goes to Mark Knopfler, lead vocals and guitar legend from the Dire Straits, for inspiring the titles of two sections, in his wonderful song.

Technorati Tags: Agile,Scrum,Sprint Planning,Estimation,User Story,Task,How To,Team

Do Agile Estimation Techniques Really Account for Scrum Projects’ Successes?

After several years of experience working with, training and coaching teams on Scrum, I have no doubt that agile (empirical) estimation techniques are better than classic, predictive estimation techniques. One question that kept nagging me over time is whether or not using story points, T-shirt sizes, or ideal hours truly explains the increased success of Scrum based techniques?

It took me a while, but I finally figured it out. And the answer is a definite ‘NO’!

All Estimation Techniques are Merely Guesses

At the end of the day (or at least, at the end of the planning session), any estimation is just a guess: sometimes its more, and sometimes its less. This means that the distribution of the actual time versus the estimation should be pretty much normal: In large numbers, about half of the actual results will be less than the estimation, and half will be more. On average, according to the Law of Large Numbers, it evens up.

The better the estimation technique is, the steeper the curve will be, i.e. the closer actual results will be to the estimation.

My experience is that agile techniques are in fact better at predicting the actual results, but the fact remains that if you make enough of these predictions, then regardless of the technique, the variance should cancel itself out and the sum should settle on the average!

This means that the quality of the technique used to estimate each task has little or no impact of the success of estimating the time to complete the entire project!

Then Why Do Estimations Fail?

If estimations behave “normally” whole project estimations should not fail. I don’t buy into all that “programmers are notoriously optimistic” sentiment; I’m a developer myself and there is not a single soul alive that will call me optimistic, and yet, non-agile projects I worked on overran their deadlines, like everybody else’s. Something must be out there, that either changes the normal distribution, or invalidates the law of large numbers or both. And it has to be something that is present in classic waterfall-ish projects but not in agile ones.

It Is All About the Independence!

The main difference, in my opinion between agile and non-agile projects is how they are planned and constructed. An agile project plan is basically a stack of user stories, with no* dependencies between them, whereas a waterfall project plan is a Gantt chart describing tasks and their interdependencies.

Why Is This Important?

Let’s assume a task that has two dependencies. Each were estimated, and each have a normal distribution of the actual amount of time to complete vs. the estimated time. The following figure describes such a “plan”:

Each task has a 50% chance to be completed before time and 50% to be completed after (There is of course no statistical chance of being completed “on time”). Now, let’s say we’re interested in calculating the probability of Task C to be completed before time, or not to finish late. For the sake of ease, we’ll assume all three tasks to have the same estimated duration. For Task C to begin early, it needs both of its dependencies to complete early. Since the chance of each to complete early is 50%, the chance for both is (0.5 * 0.5) = 0.25, or 25%. The chance for this not to happen, i.e. for at least one of them to be completed late is 1-0.25 or 75%. this means that there’s a 75% for Task C to complete late, and of course it has its own probability of being late, which means that the chance of Task C completing late (and making the whole project run late) is more than 75%!

It is easy to see that the more complex a project is, the more dependencies each task has the greater the chance of being late is; The distribution of the actual duration of tasks with dependencies is a negatively skewed distribution.

Scrum User Stories Have No Dependencies

With this in mind, Scrum user stories have no* interdependencies. If two stories do have a dependency, they are should be joined to be treated as one (and if they are too big, they can and should be split again, though in a way that doesn’t create dependencies). With no dependency (or, if you wish, a single dependency that is the completion of the prior story), we get a plan that looks like this:

The chance of B starting early, which is the same chance of A ending early, is 50%. The chance of B completing early is still 50%! Even better the variance is reduced, which means that there is an increased probability of the total project duration being close to the the estimate (i.e. a steep probability distribution).

What Does This Mean?

This means, that while a project with multi-dependent tasks has an increased skew towards lateness with each added task, a project with no dependency (beyond sequence) has an increased tendency towards the mean, or the estimate.

This means that by the very nature of what we are estimating, agile projects tend to succeed (i.e. be on budget and on time), than non-agile.

Moreover, the actual estimation technique doesn’t matter – it is the estimation of independent units of work that makes the difference.

Therefore, my conclusion, and advice is that the very first change a team needs to make is to start working on independent features – call them user stories, features, or MMFs – it doesn’t matter; just keep them independent of each other!

Hope this helps,

Assaf.

Technorati Tags: Agile,Estimation,Scrum,Sprint Planning,User Story

How to Use Agile Estimation in Scrum Projects

Like it or not, estimations are a big part of professional and commercial software development. Estimations are used in most projects regardless of methodology: Waterfall or Scrum, traditional or agile, every stakeholder needs to know the numbers. Projects succeed or fail as a result of these. Unfortunately, humans, as a rule, tend to suck at estimating.

This post will compare and contrast the different estimation techniques and applications used by both agile and non-agile methodologies, and explain how to use them (and not misuse them), particularly in a Scrum project.

What is Wrong with how We Estimate?

Traditional project plans involve using calendric estimates in order to synchronize the development efforts of the team(s) by placing the work on a time line (usually, using a Gantt chart). This means that for every part of the solution, the developers must predict the when it will be ready (i.e. the delivery date). This means they need to know when they will start, how much time it will take to deliver the work, and how much time will be “wasted” on impediments (both foreseeable and unforeseeable). While knowing this makes the project planners’ work easier, obtaining this knowledge is extremely difficult; Estimating the development effort is difficult enough, but predicting the total amount of time that will be spent not developing is near impossible.

What’s worse, is that contrary to popular belief, the Law of Large Numbers does not apply to project plans! Due to the nature of dependencies, a single late dependency is enough to push the delivery date of a dependent component. On the other hand, in order for a component to be delivered early, it is necessary that all dependencies will be completed early. In short, lateness aggregates, while earliness does not.

A side effect of this is that the more complex the Gantt based project is, the more lateness is likely to be accumulated, as the project advances, and the further we are in the project, the more we run the risk of being late.

Of course, a late project means that the software is delivered at a higher cost, with less features, and / or simply later than expected and agreed. This means that a late project often becomes a failed project.

Clearly, a different method of doing things is necessary; Agile methodologists came up with different estimation techniques to reduce the chances and ramifications of failure.

Agile Estimations Are Easier to Make

The three most common agile estimations are T-Shirt Sizes, Ideal Time, and Story Points:

• T-Shirt Size estimations are coarse grained separation of requirements into buckets of small, medium, large, or very-large efforts. This estimate is of course extremely easy to make intuitively, and of course insufficient as a basis for project planning due to its low resolution, but is often sufficient for the purpose of pricing single features (example scenario might be when required to give a customer a cost estimate for some custom feature in the system).
• Ideal Time estimates are similar to calendric estimates in that they involve high resolution predictions of the effort involved in developing a feature. The difference is that ideal estimates are made while ignoring all surprises and interferences! The question that ideal estimates answer is “how much time will it take to develop a solution, assuming you work on nothing else, and nothing else disturbs you”. Taking the unknowable impediment factor out of the equation improves the accuracy of the estimates, but you can’t simply substitute ideal time for calendric time because the unknown impediments still do exist, and the fact remains that humans aren’t good at making absolute estimates, with no point of reference. In Scrum, Ideal Hours are often used to estimate the effort of completing tasks, which should be short, simple and straightforward.
• Story Points are a radically different method of estimating development effort. With story points you do not go out and say how much work something is, in and of itself, but rather state how the effort to develop one component compares to the effort of developing another. These estimates are much easier to make, and surprisingly accurate because we are much better at comparing things than estimating them. Of course, comparisons and relationships have no unit of measure, and thus can’t be used to establish a timeline. At least not in the normal sense. In Scrum, Story Points are usually used to estimate the effort of completing user stories (hence the name).

Agile Estimation Techniques Make Better Plans

While agile estimation techniques tend to end up more accurate than calendric estimates, the estimations are still guesses, and guesses still mean risk. Agile teams try to reduce this risk by reducing the amount of guesswork involved in the project plan.

Classic project plans involve estimating the time it will take to deliver each component, and add up all the estimates to create one big guess-of-a-plan. While agile project plans involve estimating the work on each component as well, that is where the similarity ends.

The agile project plan is not based on the prediction of work effort, but rather on empirical evidence! Only the team’s first iteration (ever) is planned based on a prediction. The rest are based on the measured evidence of previous iterations (or sprints, as they are often called in Scrum-shops). The team estimates that it will successfully complete as much work in the next iteration as it did in the previous iteration(s).

For example, if a team successfully completed 120 ideal hours worth of work (that is, the amount of work they did added up to what they estimated as 120 ideal hours), then they will estimate that they will do the same in the next iteration. This technique works the same with story points: A team that can complete an average of 50 story points in an iteration is likely to repeat that success in the next one. A team’s velocity, or cadence can then be measured in ideal hours per iteration or story points per sprint.

What about interruptions? Well here’s the beauty of the technique: You can consistently account for the predictable ones (e.g. vacations, repeat meetings, scheduled events) and factor those out, and you can consistently ignore the unplanned ones, as they are likely to be more or less an equal part of every iteration of the team.

Where’s the Difference?

The difference between predictive plans (classic, waterfall-ish, Gantt based) and empirical plans (Agile, Scrum) are:

1. Predictive plans are based on more estimation (i.e. guesses, i.e. risk) than empirical ones
2. Empirical plans have an increasingly sound statistical ground as the project progresses
3. Predictive plans have an increasing chance of running late, as the project progresses
4. With predictive plans the unknown factors aggregate, with empirical plans they cancel each other out.

Finally, as stated in the previous post, agile plans, being based on stories that are designed to be discrete units of business-value, with as little interdependencies as possible, agile plans further mitigate the risk of estimation failure by causing the ramification to be failure to deliver on time only the least valuable features!

In Conclusion

To me, the agile choice is a no brainer. Agile plans are simply safer to execute, the chance of failure is lesser, and the result is less harmful. I would even go as far as to say that companies promising to deliver a non-trivial project without using an agile plan of some sorts are being less than honest – with themselves as well as the customer.

Moreover, I think it is high time that customers demand their contractors to use agile methods in order to win contracts.

Technorati Tags: Agile,Estimation,Scrum,Release Planning,Sprint Planning,Story Points,Ideal Hours

Demystifying Agile: How to Plan Releases with Scrum

The agile release process might sometimes seem like voodoo (or even dark voodoo) to some managers and developers, particularly those more accustomed to the traditional way of planning processes used in waterfall projects. This post will explain the inherent problems with waterfall projects, how agile methodologies such as Scrum overcome these problems, and of course, the reasons that the agile release planning method works.

You Can’t Control Everything

The PMBOK call it the project management triangle; the agile community call it the Iron Triangle. Regardless of the name, it is a real and hard constraint: Of the three aspects of the project – scope, resources (or cost), and time you may pick and control any two. The third cannot be controlled without harming the quality, and is calculated (roughly) by the following formula:

This means that if you have a project where the budget is fixed, and there is a hard deadline (i.e. release date), then you cannot control the scope that will be delivered by the team by the time of the release.

Moreover, Brook’s Law, states that adding people to a late (i.e. already started) project will only make it later. According to this, since the cost of a software project is mostly the cost of the team members, the resources must remain fixed for the project (or release).

Nevertheless, traditional project managers seem to try to define the entire scope of a project, define the releases and milestones (i.e. the time), with a fixed preset budget (i.e. cost).

It is simply doomed to fail. According to the Standish Group and the Chaos Report, most projects (68 percent) do.

Why do Projects Fail?

According to Martin Fowler, it is not the projects that fail, but rather the estimates that fail; it is our attempt to calculate how much time it will take to deliver a certain scope with a certain amount of resources.

I would argue, that the failure to estimate is the reason that projects fail. We really don’t know how to estimate. Projects fail because they rely too heavily on the estimates.

How do Agile Methods Reconcile with the Iron Triangle?

Given values such as courage and honesty, it is no surprise that agile methodologists simply acknowledge that estimation is not a good basis for a release plan, and search for alternatives.

Given the Iron Triangle, Brook’s Law, and the fact that most customers demand a hard release date, an agile PM accepts that he cannot control the scope that will be delivered; that is, while the PM may make guesses (i.e. estimates) as to the content that will be delivered on the deadline, he cannot be sure that every last task will be completed.

What he can promise, and make certain of, is that he will deliver the best product possible on the release date, with the rest of the desired features in consecutive releases.

How do Agile Methods Deliver the Best Product on Time?

By slicing the entire scope into vertical slices, small increments in valuable functionality (often called user stories), the agile PM now has a stack of user stories to be released, or a product backlog. By defining the stories in such a way that they have as few interdependencies as possible, the PM may order them as he desires. The PM will of course, choose to order them by business value to the customer.

The development team, in turn, will swarm on each story, by order of value (priority), and focus on completely delivering one story at a time (completely means coding, testing, integrating and anything else required).

In this way, at any given moment, including the release date, the team has a product that is shippable and is the most valuable product they could possibly develop.

Is the Best Product Possible Good Enough?

While the best product might not be everything that was envisioned, because again – that is simply not possible: not with waterfall, not with agile, not anyway – the “Best Product” will have the capabilities most valued by the customer.

The findings of the Standish Group show that only 20% of the features in any given product are frequently used, while 64% are rarely or never used. Therefore, statistically speaking, even if you only complete 36% of your scope, you may still have a good enough product!

Of course, assuming that you aren’t slacking, and the project’s deadline isn’t insanely short, it is more likely that you will complete a significantly larger portion of your scope. It is even possible that you will have a “good enough” product that you can ship even before the deadline! There is not a single customer that will say no to reducing time to market!

So I’d say, yes – the Best Product is good enough.

And that’s how it’s done. I hope

How about your products? Do you work this way? Are your experiences similar? Share!

Thanks,

Assaf.

Technorati Tags: Agile,Estimation,Scrum,User Story,Release Planning,Sprint Planning

How to Measure Technical Debt

Or: An Executive’s Guide to the Cost of Software Architecture Shortcuts

In my experience, in any software project of sufficient complexity, it is necessary to cut corners in the software architecture. These shortcuts are decision that you make now so that you will be able to release your next version of the software that much earlier; these shortcuts are the result of not having enough time to do things “right”.

This is called your project’s Technical Debt.

Like financial debt, which is the metaphor used to explain it, technical debt happens when you “buy” more software features than you have resources (developers and time) for. Like financial debt, it accrues interest over time. And like financial debt, if you don’t pay it back, you may eventually get foreclosed and go bankrupt. That last part is not a metaphor. Your software project simply ends up costing more to maintain than the value you derive from it, and either the project or the company gets shut down, or both.

There are essentially two ways to deal with debt (technical, like financial):

1. You pay it all at once – This means that you do not “withdraw” any more features, until your debt has been fully paid. It has the benefit of being able to make a fresh start. It has the disadvantage of risking “starvation” while you try to pay the “bank”.
2. You pay it in installments – This means that you pay off your debt, a small part at a time, still “withdrawing” features, but less than you normally would, cutting down to essentials, for as long as you are still paying off the debt. It takes longer to finish, but you can still “eat” while paying.

Of course there’s the third option of continuing to withdraw as before, until you go broke, or luckily win the ‘lottery” (investment, buy-out, etc.)

There is no one clear way to deal with debt (financial or technical). It will depend on the situation, and the players. And most of all it will depend on the amount of debt.

Quantifying the Debt

With financial debt, it is easy to determine how much you owe. There’s the amount of money you borrowed, and the interest rate. Both are usually well known, and it is just a matter of keeping track of it, to make sure you can withstand it.

With technical debt, you still have the amount you borrowed, and the interest rate, but you usually don’t know the numbers.

Let’s start with figuring out the derived value of taking the loan. Whenever you demand that a feature or set of features be completed faster than it would normally take if the team were to maintain an appropriate quality level, you are “borrowing” from the architecture. Figure out how much time you saved, and multiply it by the hourly cost of the developers. That is the amount of money you borrowed from the architecture. Figure out what the reduction in the Time to Market will earn you, and that is the derived value of the loan.

The interest rate is not so easily calculated a-priori, but it can be estimated (with about as much accuracy as the development cost estimates your team makes – which may be good or bad; depends on your experience):

1. Map out the areas impacted by the shortcut
2. Estimate how much it would cost (developers x time) to develop a feature in the impacted areas, before the shortcut(s)
3. Now, estimate the cost to develop the same feature after introducing the technical debt

The difference in the cost is the interest you are paying. Keep track of the interest as it will change with every change you make to the system. A good practice is to calculate the interest every iteration or sprint. You should consider doing this as part of the sprint planning. This will have the added benefit of giving the product owner the opportunity to pay off some of the debt.

Note that like financial debt, the interest grows over time. The rate varies, and it is difficult to guess what it will be, except by projecting based on past experience. As you begin to accumulate this data, you will be able to make educated decisions on when you can afford to go into technical debt, and when you should avoid it. You should reassess your technical debt’s interest rate every sprint planning.

Armed with this knowledge, developers should now be able to collaborate with management in order to decide how to develop and whether or not it is okay to cut a corner on that next feature.

Finally, remember: Your architecture is a cold blooded loan shark, who will just as soon take your installments, as he will bust your project’s kneecaps over a missed payment.

What do you think? Do you have a preferred method of calculating / estimating technical debt, or a way to handle it?

Please share!

Technorati Tags: Agile,Estimation,Scrum,Sprint Planning,Technical Debt

How to Break a User Story into Tasks

One of the vaguest aspects of Scrum is breaking stories into tasks, which is done in (the second half of) the Sprint Planning. Most of the questions I get when training or coaching boil down to one question: “How do I break a user story into tasks”?

Defining The Problem

My favorite definition for a user story is that it is a thin, vertical slice of functionality, describing a small increment in value to the user (or customer).

A task, on the other hand is a step taken by the development team, required to fulfill the requirements of the user story. I further like to define, as a rule of thumb, that a task should take half a day to 2 days to complete.

The question therefore should be what are the steps the team needs to take for the story to be considered complete?

Look at Your Architecture

Explicitly or implicitly, your project has an architecture. It might be anything from a master piece drawn out in painstaking elaboration by a the architecture team, to a high-level conceptual architecture diagram. You should look at the components in your system, and ask your self which components do you need to modify (or add) for the story.

For example, Let’s say that we have a product that is a social networking web site of some sorts. given a story such as In order to broaden my network, as a user, I want to be able to import contacts from Gmail. Looking at a typical architecture for such a product, my tasks could be something like:

• Add a new Import Contacts page
• Add a service to get contacts from Gmail
• Modify the contact class to accommodate Gmail specific data (let’s assume such exists)
• Modify the contacts data schema

Assuming these tasks are right-sized (4-16 hours), that’s it. If one of these tasks is bigger, break it down. If a task is smaller, merge it with a related one (if such exists. If not, don’t sweat it. My 4-16 rule is just a guideline). We now have all the tasks for us to complete the coding of the story.

Look at the Definition of Done

Of course, just coding your story isn’t sufficient for the story to be truly done. In Scrum, “done” means ready to ship, or as I like to define it: the story is as it will be received by the customer / user. Agile teams have a list of requirements that should  be true for any story they complete. You should look at your Definition of Done (DoD) to see what tasks remain.

For example, let’s say your DoD includes the following:

• Code Complete
• Automated UAT (User Acceptance Tests)
• Documentation is updated
• Added to shipping package (e.g. InstallShield)

In this case, you should add tasks that help you achieve the story’s DoD. For the above example you might do the following:

• Write UATs for the story
• Document the Contacts Import feature

Assuming you didn’t have to add any new assemblies (i.e. libraries, packages, DLLs, executables, etc.), to your installation, you probably wouldn’t have any need to modify your packaging tool. No need to add a task for that.

That’s It!

You now have 6 tasks for the story:

• Add a new Import Contacts page
• Add a service to get contacts from Gmail
• Modify the contact class to accommodate Gmail specific data (let’s assume such exists)
• Modify the contacts data schema

• Write UATs for the story
• Document the Contacts Import feature

You can now estimate how much time each one will take, and are ready to move on, and break down the next story.

Technorati Tags: Agile,Scrum,Tasks,Sprint Planning,User Story,Definition of Done