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Bayesian Networks for Software Process Control

Although a number of approaches have been taken to quality prediction for software, none have achieved widespread applicability. Our aim here is to produce a single method to combine the diverse forms of, often causal, evidence available in software development in a more natural and efficient way than done previously. We use graphical probability models (also known as Bayesian Networks) as the appropriate formalism for representing this evidence. We can use the subjective judgements of experienced project managers to build the probability models and use these models to produce forecasts about the software quality throughout the development life cycle. Moreover, the causal or influence structure of the model more naturally mirrors the real world sequence of events and relations than can be achieved with other formalisms. The work described was the result of an extended research programme involving Philips and a number of other partners. The work led to both a set of easily tailorable models and a tool (AgenaRisk) that enables the models to be run efficiently by non-expert end-users. We describe the results of extensive industrial evaluation. In particular, we focus on the defect prediction models evaluated at the Philips Software Centre, Bangalore. This produced very encouraging results; a correlation of 95% between predicted and actual defects found across a highly diverse range of projects.