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

TL;DR

This paper presents a method to derive Bayesian Network models from activity-based quality models to assess and predict software quality, demonstrated through a proof of concept with publicly available data.

Contribution

It introduces a novel approach to operationalize activity-based quality models using Bayesian Networks for software quality assessment and prediction.

Findings

Successful derivation of Bayesian Networks from activity-based quality models

Effective assessment and prediction demonstrated in a proof of concept

Potential for improved software quality management

Abstract

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.