Applying machine learning to software fault-proneness prediction
Journal of Systems and Software
Software faults: A quantifiable definition
Advances in Engineering Software
Recalling the "imprecision" of cross-project defect prediction
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
How, and why, process metrics are better
Proceedings of the 2013 International Conference on Software Engineering
Estimating software testing complexity
Information and Software Technology
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Predictive models that incorporate a functional relationship of program error measures with software complexity metrics and metrics based on factor analysis of empirical data are developed. Specific techniques for assessing regression models are presented for analyzing these models. Within the framework of regression analysis, the authors examine two separate means of exploring the connection between complexity and errors. First, the regression models are formed from the raw complexity metrics. Essentially, these models confirm a known relationship between program lines of code and program errors. The second methodology involves the regression of complexity factor measures and measures of errors. These complexity factors are orthogonal measures of complexity from an underlying complexity domain model. From this more global perspective, it is believed that there is a relationship between program errors and complexity domains of program structure and size (volume). Further, the strength of this relationship suggests that predictive models are indeed possible for the determination of program errors from these orthogonal complexity domains