  Assessing and predicting the complex concept of software quality is still
challenging in practice as well as research. Activity-based quality models
break down this complex con- cept into more concrete definitions, more
precisely facts about the system, process and environment and their impact on
ac- tivities performed on and with the system. However, these models lack an
operationalisation that allows to use them in assessment and prediction of
quality. Bayesian Networks (BN) have been shown to be a viable means for
assessment and prediction incorporating variables with uncertainty. This paper
describes how activity-based quality models can be used to derive BN models for
quality assessment and pre- diction. The proposed approach is demonstrated in a
proof of concept using publicly available data.
