Bayesian networks modeling for software inspection effectiveness

Software inspection has been broadly accepted as a cost effective approach for defect removal during the whole software development lifecycle. To keep inspection under control, it is essential to measure its effectiveness. As human-oriented activity, inspection effectiveness is due to many uncertain factors that make such study a challenging task. Bayesian networks modeling is a powerful approach for the reasoning under uncertainty and it can describe inspection procedure well. With this framework, some extensions have been explored in this paper. The number of remaining defects in the software is proposed to be incorporated into the framework, with expectation to provide more information on the dynamic changing status of the software. In addition, a different approach is adopted to elicit the prior belief of related probability distributions for the network. Sensitivity analysis is developed with the model to locate the important factors to inspection effectiveness.