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Assessing and predicting the complex concept of software quality is stillchallenging in practice as well as research. Activity-based quality modelsbreak down this complex con- cept into more concrete definitions, moreprecisely facts about the system, process and environment and their impact onac- tivities performed on and with the system. However, these models lack anoperationalisation that allows to use them in assessment and prediction ofquality. Bayesian Networks (BN) have been shown to be a viable means forassessment and prediction incorporating variables with uncertainty. This paperdescribes how activity-based quality models can be used to derive BN models forquality assessment and pre- diction. The proposed approach is demonstrated in aproof of concept using publicly available data.

A Bayesian Network Approach to Assess and Predict Software Quality Using Activity-Based Quality Models (Conference Version)