A Bayesian network approach to assess and predict software quality using activity-based quality models |
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| Authors: | Stefan Wagner |
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| Affiliation: | 1. Department of Information Management, Chung Yuan Christian University, Taoyuan County, Taiwan;2. Institute of Information Management, National Chiao Tung University, Hsin-Chu, Taiwan;1. Simula Research Laboratory, P.O. Box 134, Lysaker, Norway;2. Department of Informatics, University of Oslo, Oslo, Norway;3. Brunel University, Kingston Lane, Uxbridge, Middlesex, UK;1. Informatics and Environment Laboratory, Department of Biology and Geology, University of Almería, Carretera de Sacramento s/n, C.P. 04120, La Cañada de San Urbano, Almería, Spain;2. Department of Mathematics, University of Almería, Carretera de Sacramento s/n, C.P. 04120, La Cañada de San Urbano, Almería, Spain;1. Universitat Politècnica de Catalunya, Spain;2. Fraunhofer Institute for Experimental Software Engineering, Germany;1. Department of Computing and Systems, Federal University of Campina Grande, Rua Aprigio Veloso, 882, Bodocongo, 58109 900 Campina Grande, PB, Brazil;2. Department of Electrical Engineering, Federal University of Campina Grande, Rua Aprigio Veloso, 882, Bodocongo, 58109 900 Campina Grande, PB, Brazil |
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| Abstract: | ContextSoftware 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.ObjectiveThe 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.MethodThe 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.ResultsThe 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.ConclusionThe approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity. |
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