Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.
An earlier version of this paper was published in @cite_4 . It already contained the 4-step approach as described in this paper. However, we added 3 additional projects to the maintainability case and a completely new security case in order to validate the applicability of the approach more broadly.
@cite_4
2004973017
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 concept into more concrete definitions, more precisely facts about the system, process and environment and their impact on activities 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 prediction. The proposed approach is demonstrated in a proof of concept using publicly available data.