A Bayesian network approach to assess and predict software quality using activity-based quality models

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

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 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.

Original languageEnglish
Title of host publicationPROMISE 2009 - International Conference on Predictor Models in Software Engineering
DOIs
StatePublished - 2009
Event5th International Conference on Predictor Models in Software Engineering, PROMISE '09 - Vancouver, BC, Canada
Duration: 18 May 200919 May 2009

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Predictor Models in Software Engineering, PROMISE '09
Country/TerritoryCanada
CityVancouver, BC
Period18/05/0919/05/09

Keywords

  • activity-based quality models
  • Bayesian networks
  • quality assessment
  • quality prediction

Fingerprint

Dive into the research topics of 'A Bayesian network approach to assess and predict software quality using activity-based quality models'. Together they form a unique fingerprint.

Cite this