When Estimates Go Wrong
I forgot to announce that ProjectManagement.com published my article, When Estimates Go Wrong, a couple weeks ago.
What is the Right Length for a Project?
I’ve seen many comments on the topic of estimation in the past year, and I’m starting to notice some trends and assumptions in them. One of the common assumptions is that, given a particular team and amount of resources, there is a correct length to a project. A twin to this one is that there is a correct estimate that contains the same end date as the subsequent actual project performance. Read More
Why I Practice TDD
I was reading Laurent Bossavit’s book, “The Leprechauns of Software Engineering—How folklore turns into fact and what to do about it,” and came across his mention of “Comparing the Defect Reduction Benefits of Code Inspection and Test-Driven Development” by Jerod W. Wilkerson, Jay F. Nunamaker, & Rick Mercer. This struck me as an odd thing to study. Not only is Test-Driven Development not primarily about defect reduction, but the populations of defects it might reduce are likely to be very different from population of defects reduced by code inspection.
I then took a look at my own list of TDD studies and noted that most of these studies were focused on external quality as measured by absence of known defects, and time it took to develop the functionality. Keith Braithwaite, at Agile 2007, reported on internal quality, specifically Cyclomatic Complexity.
Quality and productivity are, of course, important things. And they’re easy to sell to some managers. Who could be against them? And I certainly wouldn’t continue to practice Test-Driven Development if it added defects or took a significantly longer time to create functionality. But that’s not why I practice TDD. Read More
Managing Risk With Estimates
Used with care, software development estimates can help you manage project risks. They let you peer into the future, though only as well as your current understanding allows. Estimating based on what you know is easy. Estimating based on what you know you don’t know is possible. Allowing for what you don’t know you don’t know is prudent.
Managing risk is a dynamic process. I’ve seen people document a risk in a “Risk Register” document and promptly ignore it. That’s not management. Instead, consider different ways a risk might be reduced in likelihood or consequence. When time or cost is of the essence, think about how you’ll determine what you can afford, and when you need to take a second-choice approach. Read More
Dysfunctional Commitment
Team commitment is a wonderful and sometimes fragile thing. Many responses to my description of it are indications of how frequently the word “commitment” is used in a dysfunctional manner. Indeed, the post was prompted by similar conversations.
Believe me, I’ve seen these dysfunctions many times. They are so numerous and varied that no catalog of them could be complete. It’s not the word, commitment, that causes the problems, however. And avoiding that word will not solve the problems. Instead, we have to look at the behavior and attitudes behind the problems in order to reliably recognize them and choose strategies for correcting them. Read More
Team Commitment
Most Scrum teams estimate their top priority stories, select those stories that add up to their historical velocity for their sprint backlog. Some teams simplify this by merely counting the stories, or using the mathematical reciprocal, cycle time. Others make it more complicated, calculating the effect of days off and other known distractions from the work.
However they calculate it, some people put a lot of faith in the historical data to guide the future. “It’s data,” they say, “it’s better than guesses and not subject to cognitive bias.” Not all data is easily measured and converted to numbers, though. Limiting yourself to this initial calculation is, itself, an example of anchoring bias. Read More
Agile: What’s in it for the Project Manager?
Over on projectmanagement.com, my article “Agile: What’s in it for the Project Manager?” has been posted in two installments: part 1 on gathering requirements and work breakdown, and part 2 on interpreting requirements and tracking progress. Projectmanagement.com requires free registration to access the full content.
Estimation as Hypothesis
Experimentation is a powerful learning tool. When I was young, I performed scientific experiments by mixing chemicals together to see what they would do. I learned that most random concoctions from my chemistry set would make a brown liquid that was often hard to clean out of a test tube. I learned that sometimes they would create very smelly brown liquids. These were not really experiments, however, and I didn’t really learn from them. Instead, these were activities and I collected anecdotes and experiences from them.
The scientific method rests on the performance of experiments to confirm or deny a proposed hypothesis. Unless you can propose a hypothesis in advance, you cannot design an experiment to test it. Until you test the hypothesis, you haven’t really learned anything. Read More
How do we estimate?
There have been some web posts and twitter comments lately that suggest some people have a very narrow view of what techniques constitute an estimate. I take a larger view, that any projection of human work into the future is necessarily an approximation, and therefore an estimate.
I often tell people that the abbreviation of “estimate” is “guess.” I do this to remind people that they’re just estimates, not data. When observations and estimates disagree, you’d be prudent to trust the observations. When you don’t yet have any confirming or disproving observations, you should think about how much trust you put into the estimate. And think about how much risk you have if the estimate does not predict reality.
This does not mean, however, that you have to estimate by guessing. There are lots of ways to make an estimate more trustworthy. Read More
What Decision Are We Supporting?
In business, we’re often asked for estimates with too little context to understand the request. When that happens, we’re likely to expect the worst–that our estimate will be treated as a “guarantee not to exceed” and we’ll likely be in trouble at some time in the future. Of course we think that; we’ve been burned too many times in the past. Our fear of the consequences will encourage us to spend far too much time and effort trying to get the estimate “right” so we won’t be blamed.
If an estimate is really an estimate, then we know that it’s “wrong” in the sense that the subsequent actual reality is unlikely to equal it. The estimate is a guess, perhaps an educated guess, predicting the future. Predictions are hard, especially about the future.
Given these problems with estimates, why do we bother to make them at all? Read More