Regression via Classification applied on software defect estimation
Expert Systems with Applications: An International Journal
Estimating software readiness using predictive models
Information Sciences: an International Journal
Change profiles of a reused class framework vs. two of its applications
Information and Software Technology
An iterative semi-supervised approach to software fault prediction
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
On the use of calling structure information to improve fault prediction
Empirical Software Engineering
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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To analyze the complexity of object-oriented software, several metrics have been proposed. Among them, Chidamber and Kemerer's metrics are well-known ones as object-oriented metrics. Also, the effectiveness has been empirically evaluated from the viewpoints of estimating the fault-proneness of object-oriented software. In the evaluations, their metrics were applied to not design specification but the source code because some of them measure an inner complexity of a class, and such information can not be obtained until the algorithm and structure of the class are determined at the end of design phase. However, the estimation of the fault-proneness should be done in the early phase to effectively allocate effort for fixing the faults.This paper proposes a new method to estimate the fault-proneness of the class in the early phase, using several complexity metrics for object-oriented software. In the proposed method, we introduce four checkpoints into the analysis / design / implementation phase, and estimate the fault-prone classes using the applicable metrics at each checkpoint.