An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction| Science Publications

Journal of Computer Science

Luiz F. Capretz¹  Jie Xu¹  Danny Ho¹ 

关键词: Software quality;    design metrics;    statistical analysis;    neuro-fuzzy;    prediction;   

DOI  :  10.3844/jcssp.2008.571.577

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem Statement: Object-oriented design has become a dominant method in software industry and many design metrics of object-oriented programs have been proposed for quality prediction, but there is no well-accepted statement on how significant those metrics are. In this study, empirical analysis is carried out to validate object-oriented design metrics for defects estimation. Approach: The Chidamber and Kemerer metrics suite is adopted to estimate the number of defects in the programs, which are extracted from a public NASA data set. The techniques involved are statistical analysis and neuro-fuzzy approach. Results: The results indicate that SLOC, WMC, CBO and RFC are reliable metrics for defect estimation. Overall, SLOC imposes most significant impact on the number of defects. Conclusions/Recommendations: The design metrics are closely related to the number of defects in OO classes, but we can not jump to a conclusion by using one analysis technique. We recommend using neuro-fuzzy approach together with statistical techniques to reveal the relationship between metrics and dependent variables, and the correlations among those metrics also have to be considered.

【 授权许可】

Unknown