A major challenge for software professionals interpreting data is deciding what's real and what isn't, what matters and what doesn't. A useful way to think about it is that you are trying to find the signal in the noise produced by random variation and error. Here is advice on how to extract the useful information from the "noise."
Taking development and business contexts into consideration can mean the difference between a correct assessment and a useful assessment. Here's information on how to provide an assessment that's both correct and effective.
When you approach a process problem in the way your workgroup functions, you're implementing an organizational change. Organizations are systems of complex interrelationships. Explicit models can help you make strategic changes.
A Bayesian Belief Net is a graphical network that represents probabilistic relationships among variables. Here is a studied look at this causal modeling technique as applied to defect prediction and resource estimation.
Tracking your project goals lets you know how well your improvement program is going, provides visibility early to detect problems, and gives you data to make your future plans more effective. Here's how to measure improvement based on your project's goals and problems.
How do you really know how good your software is? Many traditional measures only look at the quantitative aspects of quality. Here's a model to measure and analyze subjective—or qualitative—data about software quality.
Improving customer satisfaction should be a primary goal of process improvement programs. So how satisfied are our customers? One of the best ways to find out is to ask them. Here are techniques for creating a useful survey and interpreting the results.