5 Reasons to Manage Your Solo Project’s ALM Process

So I’ve been developing software on the Microsoft stack for just over 10 years, and training and consulting on managing software development with VS-ALM (a.k.a. TFS, a.k.a. Team System) for just over 4 of those years. But in all time training and consulting, I was working with teams – from small 5 person start ups, to enterprises with hundreds of users, all using Team Foundation Server (a.k.a. on-premises TFS) to develop their methodology and align their tooling with their products.

Never individuals, though.

Over the years, though,  I’ve had the opportunity to fly solo on several software projects, some short lived, some longer, as have other ALM consultants that I know. One thing we all seem to have in common is that, while as ALM experts, we have all of the skills and knowhow to apply our own trade to the software we develop, we seldom – if ever – do so.

The Cobbler’s Children Go Barefoot…

… as the saying goes. It seems that this proverb applies to our profession as much as it does to others. You’ve seen the doctor who smokes, the out-of-shape coach, the technician or plumber whose own wiring or plumbing is faulty, respectively. Perhaps it’s the same. Perhaps its just that proper ALM is more visibly useful when multiple hands are involved and they must all communicate and interact properly with each other, than when one person holds all roles.

Solo projects seem to need no management, I believe they only seem to.

Here’s why:

1. If You Don’t Use Source Code Version Control, You’re an Idiot!

I apologize for the vehemence. Let me rephrase this. If you don’t use some kind of source code version control, you’re an inexcusably dense idiot who deserves what he or she gets when you lose your source code!!!

I guess I couldn’t rephrase it without the vehemence. Lesson learned.

Beyond the fact that having some kind of backup, preferably online, means that even if you lose your laptop, or accidentally delete your source files, you still have a copy of your code, version control means that if you make a mistake, you can still rollback to that point in time and fix whatever you did wrong. Having a historical log of the changes you made to the software can also serve as release notes of sort, and a rudimentary list of the features and bug fixes you have, and even help establish some kind of time line.

Really though, version control is about security for the code – protection against loss and against mistakes. you should use it as frequently as possible.

Note that you do not need to shell out huge amounts of money to be able to use version control. In fact, as in life, so with version management, the best things are free!

I prefer using Git. You can use it locally with any operating system (Windows, Linux, Mac OS), and there are multiple online tools that work and synchronize with Git, all free: GitHub, CodePlex, and (my personal favorite) Visual Studio Online. There are, of course, other version control systems, both distributed and centralized (Mercurial is another DVCS, Subversion is a great free centralized one).

Heck, even use DropBox or some other online-syncing file-storage, just beware that it won’t protect you from deleting your code, nor will it let you rollback – but at least you’

2. Defining (just enough) Work Upfront = Less Code Writing and Rewriting

Much like TDD tends to limit the amount of production-code you write, so does defining your work. In fact what is TDD, if not the definition of a small amount of work that needs to be done to the system, along with a definition of how to know when you’re done? The same holds true for defining a development task, a user story (a.k.a. a product backlog item, or PBI) or a feature; each is an elaboration of the higher level: Feature to user story, to task, to test. When you know what needs to be done, and you know when you’re done, you know exactly what the remaining work is. That saves time.

And let’s be clear. Just enough is key here. Too little, and you’ll rewrite anything that you haven’t properly thought out and planned. Too much, of course, and changes to the reality surrounding the project and your perception of reality will bring rewrite to your plans.

3. Project Planning Makes it Possible to Plan Releases

Unless you are truly working on a hobby for yourself, you still need to be able to answer at least these two questions:

1. When will it be done?
2. What will be done by a certain date?

Without estimating the work you have to do, more so if you don’t even know how much work you have, you simply will not be able to give more than a POOMA estimate.

Note that unless you live alone, you might still need to answer these questions, if only so your partner, roommate or parent will know when you will finally clear the garage (or dinner table) from your junk.

4. Automation Reduces Human Error as well as Manual Labor

Even if you are a solo programmer, two things remain invariably true for you:

1. You’re only human.
2. You’re only one human.

Being only human means that as talented as you may be, you are still prone to repetition based errors. You’ll forget to update the version number, or forget to set the build configuration to Release, or forget to run the tests, or any number of things. When I train new developers, especially in those early days when they feel that the fact that they’re constantly making errors, and more experienced developers must be demigods or some such, in an attempt to reassure them (and lighten the atmosphere), I tell them this:

• There are only two kinds of developers: experienced, and inexperienced developers.
• Inexperienced developers make only two kinds of mistakes: Mistakes born of inexperience, and mistakes born of stupidity.
• Experienced developers make mistakes only born of stupidity

Aside from the fact that this is inherently true (an experienced person, by definition, cannot make a mistake born of inexperience), it is important to note – experienced developers make mistakes!

Computers do not. Ever.

Another saying that I’m fond of repeating, the former statement being a key reason to do so, especially when consulting and or training clients on the use of ALM tools, is to never let a human do a machine’s work (and vice versa). Anything a machine can do, we shouldn’t.

Finally, there’s the fact that any time spent on doing what someone, or more importantly, something (i.e. your computer) could do, is time not spent on things only you can do (i.e. developing the product – which you’d probably prefer to do anyway). Ask yourself this – while you’re manually doing repetitive work – who is coding? Nobody.

This holds true for building, for testing, for releasing and deploying, and basically for anything that you can (and know how to) automate.

In all but the simplest and most straightforward scenarios (usually short-term, one-shot projects), you should find ways to delegate work to a machine.

5. Defining the Work Makes Change Management Easier

It’s all great when you’re hacking away at that pet-project of yours and you’re in the zone, but a week later, a month or even three later, once you start delivering a second or third release of your software, keeping track of what was done, when – not to mention writing release notes, becomes extremely difficult, if you don’t know what your changes are or were. In fact, if you use an ALM tool for tracking features, changes and defects, and associate them with automated builds, you can generate a release notes document automatically with every build, and not have to deal with it yourself.

The other kind of change, is change of management. Today this may be your solo project. Tomorrow, who knows? You may take on a partner, give someone else the code to maintain, or decide to open the source. If the only definition of the work is whatever lies inside that wonderful brain of yours, hand-off will be a bear. Trust me. I’ve had to waste two weeks once receiving a project, and I still don’t know everything about it. Finding out stuff on my own is not the best use of my time. Having a nice set of features, tasks and bugs (past and open both) shortens the time, and makes discovery pains all but gone.

Conclusion

In summary, I’d like to state my opinion that in most (again, all but the simplest and most straightforward one-shot) software projects, proper ALM is as useful and important when flying solo as when developing with a team. Lack of communication issues (though see point #5) are balanced by the increase of work of having to do it all by yourself.

Just do it right the first time. You’ll thank me for it.

Best Regards,
Assaf Stone
Occasional Solo Developer

Technorati Tags: ALM,Software,Development,Testing,Building,Code,VCS,Git,Solo

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