I recently read the article The sad graph of software death by Gregory Brown.
Brown describes a software project wherein tasks are being opened faster than they are being closed in the project’s task tracker. The author describes this as “broken”, “sad”, and “wasteful.” The assumption behind the article seems to be that there is something inherently bad about tasks being opened faster than they are being closed.
The author doesn’t explain why this is bad, and to me this article and the confused discussion it prompted on Reddit are symptomatic of the fact that most people don’t have a clear idea of the purpose of software project management.
Another symptom is that so many software projects run into problems, causing tension between engineering, product, and other parts of the company. It is also the reason there is such a proliferation of tools that purport to help solve the problem of project management, but none of them do because they don’t start from a clear view of what exactly this problem is.
Two complimentary goals
In my view, the two core goals of project management are prioritization and predictability.
Prioritization ensures that at any given time, the project’s developers are working on the tasks with the highest ratio of value to effort
Predictability means accurately estimating what will get done and by when, and communicating that with the rest of the company.
A task tracker maintains a record of who is currently working on specific tasks, which tasks are completed, and the future tasks that could be tackled. As such, the trackers do not address the two core goals of project management directly.
I have actually thought about building a project management tool that addresses these goals, i.e. prioritization and predictability, much more directly than is currently the case with existing systems. Unfortunately, to date the value to effort ratio hasn’t been high enough relative to other projects 🙂
When a task is created or “opened” in a task tracker, this simply means “here is something we may want to do at some point in the future.”
Opening a task isn’t, or shouldn’t be, an assertion that it must get done, or must get done by a specific time. Although this might imply that some tasks may never be finished, that’s ok. Besides, a row in a modern database table is very cheap indeed.
Therefore, the faster rate at which tasks are opened rather than closed is not an indication of a project’s impending demise; rather, it merely reflects the normal tendency of people to think of new tasks for the project faster than developers are able to complete those tasks.
Once created, tasks should then go through a prioritization or triage process; however, the output isn’t simply “yes, we’ll do it” or “no, we won’t.” Rather, the output should be an estimate of the value provided to complete the task, as well as an estimate of the effort or resources required to complete it. Based on these two estimates, we can calculate the value/effort for the tasks. It is only then that we can stack-rank the tasks.
Estimating value and effort
Of course, this makes it sound much simpler than it is. Accurately estimating the value of a task is a difficult process that may require input from sales, product, marketing, and many other parts of a business. Similarly, accurately estimating the effort required to complete a task can be challenging for even the most experienced engineer.
There are processes designed to help with these estimates. Most of these processes, such as planning poker, rely on the wisdom of crowds. These are steps toward the right direction.
I believe the ultimate solution to estimation will exploit the fact that people are much better at making relative, rather than absolute, estimates. For example, it is easier to guess that an elephant is 4 times heavier than a horse, than to estimate that the absolute weight of an elephant is 8000 pounds.
This was recently supported by a simple experiment that I conducted. First, I asked a group to individually assign a number of these relative or comparative estimates. Then, I used a constraint solver to turn these into absolute estimates. The preliminary results are very promising. This approach would almost certainly be part of any project management tool that I might build.
Once we have good estimates for value/effort, we can then prioritize the tasks. Using our effort estimate, combined with an understanding of the resources available, we can come up with better time estimates. This will enhance predictability that can be shared with the rest of the company.
I have had quite a bit of experience with Pivotal Tracker, which I would describe as the “least bad” project management tool. Pivotal Tracker doesn’t solve the prioritization problem, but it does attempt to help with the predictability problem. Unfortunately, it does so in a way that is so simplistic as to make it almost useless. Let me explain.
Pivotal Tracker assumes that for each task, you have assigned effort estimates which are in the form of “points” (you are responsible for defining what a point means). It also assumes that you have correctly prioritized the tasks, which are then placed in the “backlog” in priority order.
Pivotal Tracker then monitors how many points are “delivered” within a given time period. It then uses these points to project when future tasks will be completed.
The key problem with this tool is that it pretends that the backlog is static, i.e. that new tasks won’t be added to the backlog before tasks are prioritized. In reality, tasks are constantly being added to any active project, and these new tasks might go straight to the top of the priority list.
Nevertheless, the good news is that Pivotal Tracker could probably be improved to account for this addition of new tasks without much difficulty. Perhaps a third party could make these improvements by using the Java library I created for integrating with PT’s API. 🙂
Breaking down tasks
Most tasks start out as being quite large, and need to be broken down into smaller tasks, both to make it easier to divide tasks among developers, but also to improve the accuracy of estimates.
However, there isn’t much point in breaking down tasks when nobody is going to start work on them for weeks or months. For this reason, I advise setting time-horizon limits for task sizes. For example, you might say that a task that is estimated to be started within three months can’t be larger than 2 man-weeks, and a task to be started within 1 month cannot be larger than 4 man-days.
As a task crosses each successive time-horizon, it may need to be broken into smaller tasks (each of which will, presumably, be small enough until they hit the next time horizon). In practice this can be accomplished with a weekly meeting, that can be cancelled if there are no tasks to be broken down. We would assign one developer to break down each oversized task and then the meeting would break up so that they could go and do that. Typically each large task would be broken down into 3-5 smaller tasks.
This approach has the additional advantage that it spreads out the process of breaking down tasks over time and among developers.
So how do you decide who works on what? This is fairly simple under this approach. Developers simply pick the highest priority task that they can work on (depending on skill set or interdependencies).
At OneSpot, when we broke down tasks, we left the subtasks in the same position in the priority stack as the larger task they replaced. Since developers pull new tasks off the top of the priority list, this has the tendency to encourage as many people as possible to be working on related tasks at any given time, which minimizes the number of projects (large tasks) in-flight at any given time.
To conclude, without a clear view of the purpose of successful project management, it is not surprising that so many projects flounder with many project management tools failing to hit the mark. I hope I was able to provide the beginnings of a framework to think about project management in a goal-driven way.