Implications for Real Projects

What are the implications of this distribution for real projects? Because the peak of the curve lies at approximately 0.6 sigma, we see that the most likely outcome (as measured by the curve's height) is an unsuccessful project! In fact, if the peak were exactly at 0.5 sigma, your probability of success would be only around 16 percent:

50% - 1/ 2(68%) = 50% - 34% = 16%

Because the peak is not at 0.5 sigma but closer to 0.6 sigma or 0.7 sigma, the probability of success is a little higheraround 20 percent.

Now this is starting to become very interesting, because the Standish CHAOS report[7], of which I have always been somewhat skeptical, implies about a 20 percent success rate.[8] I will have more to say about this report later on. But it is interesting to note that the lognormal distribution predicts the Standish metric as the most likely outcome, which may mean that most development projects have a built-in difficulty factor that causes the lognormal distribution to obtain.

[7] See, for example,

[8] One quotation from the report is: "…the software success rate is 24 percent overall, with numbers even lower for large projects, especially those in the government sector." In another place, we find the notion that "…only 16 percent said they consistently meet scheduled due dates." So I have two numbers: 16 percent using one definition and 24 percent using another. Because I am biased somewhat towards large projects, I derate the 24 percent to 20 percent anyway, which leads to the assertion. Note that we probably can't distinguish 20 percent from 25 percent given all the different criteria people use for defining "success." At any rate, success rates in this range are nothing to crow about, either way.

The Software Development Edge(c) Essays on Managing Successful Projects
The Software Development Edge(c) Essays on Managing Successful Projects
Year: 2006
Pages: 269 © 2008-2017.
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