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Bayesian networks modeling for software inspection effectiveness

Y. P. Wu, Q. P. Hu, K. L. Poh, S. H. Ng, M. Xie

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Software inspection has been broadly accepted as a cost effective approach for defect removal during the whole software development lifecycle. To keep inspection under control, it is essential to measure its effectiveness. As human-oriented activity, inspection effectiveness is due to many uncertain factors that make such study a challenging task, Bayesian Networks modeling is a powerful approach for the reasoning under uncertainty and it can describe inspection procedure well. With this framework, some extensions have been explored in this paper. The number of remaining defects in the software is proposed to be incorporated into the framework, with expectation to provide more information on the dynamic changing status of the software. In addition, a different approach is adopted to elicit the prior belief of related probability distributions for the network. Sensitivity analysis is developed with the model to locate the important factors to inspection effectiveness.
Original languageEnglish
Title of host publicationProceedings - 11th Pacific Rim International Symposium on Dependable Computing, PRDC 2005
Pages65-71
Volume2005
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event11th Pacific Rim International Symposium on Dependable Computing, PRDC 2005 - Changsha, Hunan, China
Duration: 12 Dec 200514 Dec 2005

Publication series

Name
Volume2005

Conference

Conference11th Pacific Rim International Symposium on Dependable Computing, PRDC 2005
PlaceChina
CityChangsha, Hunan
Period12/12/0514/12/05

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