Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97886
Authors: 
Xu, Jie
Capretz, Luiz Fernando
Ho, Danny
Year of Publication: 
2011
Citation: 
[Journal:] The International Journal of Management Science and Information Technology (IJMSIT) [ISSN:] 1923-0273 [Publisher:] NAISIT Publishers [Place:] Toronto [Year:] 2011 [Issue:] 1-(Jul-Sep) [Pages:] 1-26
Abstract: 
Quality management in Open Source Software (OSS) has become a heated topic since the open source development model emerged. Much work has been done on exploring the distinct quality attributes in OSS, but very few studies covered quality estimation. In this paper, a general procedure is proposed to derive software quality estimation models for OSS projects and various candidate techniques are suggested for individual steps. The purpose is to build a model that estimates the number of defects in a project. Several statistical techniques and a machine learn­ing approach are used to examine the significance of quality predictors. Moreover, a neuro-fuzzy approach is adopted to improve accuracy of the estimation model. This procedure is followed and validated based on data from OSS projects.
Subjects: 
software quality
quality estimation
software metrics
regression
neurazl networks
fuzzy logic
Document Type: 
Article

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