Improved Software Defect Prediction

Although a number of approaches have been taken to quality prediction for software, none have achieved widespread applicability. This paper describes a single model 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 Bayesian Networks as the appropriate formalism for representing defect introduction, detection and removal processes throughout any life-cycle. The approach combines subjective judgements from experienced project managers and available defect rate data to produce a risk map and use this to forecast and control defect rates. Moreover, the risk map more naturally mirrors real world influences without any distracting mathematical formality. The paper focuses on the extensive validation of the approach within Philips Consumer Electronics (dozens of diverse projects across Philips internationally). The resulting model (packaged within a commercial software tool, AgenaRisk, usable by project managers) is now being used to predict defect rates at various testing and operational phases. The results of the validation confirm that the approach is scalable, robust and more accurate that can be achieved using classical methods. We have found 95% correlation between actual and predicted defects. The defect prediction models incorporate cutting-edge ideas and results from software metrics and process improvement research and package them as risk templates that can either be applied either offthe-shelf or after calibrating them to local conditions and to suit the software development processes in use.