Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-8964
|Title:||A Bayesian network approach to assess and predict software quality using activity-based quality models|
|metadata.ubs.konferenzname:||International Conference on Predictor Models in Software Engineering (5th, 2009, Vancouver)|
|metadata.ubs.publikation.source:||Proceedings of the 5th International Conference on Predictor Models in Software Engineering : PROMISE '09, Vancouver, BC, Canada, May 18-19, 2009. New York, NY : ACM, 2009. - ISBN 978-1-60558-634-2. - Article no. 6|
|metadata.ubs.bemerkung.extern:||© ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 5th International Conference on Predictor Models in Software Engineering (PROMISE '09). http://doi.acm.org/10.1145/1540438.1540447|
|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.|
|Appears in Collections:||15 Fakultätsübergreifend / Sonstige Einrichtung|
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