A Bayesian belief network for assessing the likelihood of fault content

To predict software quality, we must consider various factors because software development consists of various activities, which the software reliability growth model (SRGM) does not consider. In this paper, we propose a model to predict the final quality of a software product by using the Bayesian...

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Bibliographic Details
Published in14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003 pp. 215 - 226
Main Authors Amasaki, S., Takagi, Y., Mizuno, O., Kikuno, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2003
Subjects
Bayesian methods
Electrical capacitance tomography
Fault detection
Information science
Predictive models
Programming
Software quality
Software reliability
Software testing
System testing
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ISBN0769520073
9780769520070
ISSN1071-9458
DOI10.1109/ISSRE.2003.1251044

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Summary:To predict software quality, we must consider various factors because software development consists of various activities, which the software reliability growth model (SRGM) does not consider. In this paper, we propose a model to predict the final quality of a software product by using the Bayesian belief network (BBN) model. By using the BBN, we can construct a prediction model that focuses on the structure of the software development process explicitly representing complex relationships between metrics, and handling uncertain metrics, such as residual faults in the software products. In order to evaluate the constructed model, we perform an empirical experiment based on the metrics data collected from development projects in a certain company. As a result of the empirical evaluation, we confirm that the proposed model can predict the amount of residual faults that the SRGM cannot handle.
ISBN:0769520073
9780769520070
ISSN:1071-9458
DOI:10.1109/ISSRE.2003.1251044