The Value of Lessons Learned In Improving Estimates

July 10, 2009 · Filed Under Software Estimating · Comment 

The latest issue of the IEEE transactions on  software engineering has an article discussing the value of lessons learned in improving software estimates.  I have been on the run and unable to read the entire article yet (I will update this once the article has been absorbed).  But the conclusion seems to say lessons learned reviews do not help.  

While this sounds like heresy, I believe they are comparing such reviews to personal experience.  I would have to conclude myself that personal experience is better than someone else’s lessons learned.  But so what?

Lessons learned are an important part of a viable process and essential for larger organizations.

Here is the abstract:

Inaccurate estimates of software development effort is a frequently reported cause of IT-project failures. We report results from a study that investigated the effect of introducing lessons-learned sessions on estimation accuracy and the assessment of uncertainty. Twenty software professionals were randomly allocated to a Learning group or a Control group and instructed to estimate and complete the same five development tasks. Those in the Learning group but not those in the Control group were instructed to spend at least 30 minutes on identifying, analyzing, and summarizing their effort estimation and uncertainty assessment experience after completing each task. We found that the estimation accuracy and the realism of the uncertainty assessment were not better in the Learning group than in the Control group. A follow-up study with 83 software professionals was completed to better understand this lack of improvement from lessons-learned sessions. The follow-up study found that receiving feedback about other software professionals’ estimation performance led to more realistic uncertainty assessments than receiving the same feedback of one’s own estimates. Lessons-learned sessions, not only in estimation contexts, have to be carefully designed to avoid wasting resources on learning processes that stimulate rather than reduce learning biases.

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