Abstract: Many object-oriented metrics were proposed to assess the quality of the software design such as the fault-proneness and the maintainability of classes. Software metrics can serve many purposes for software engineers. Many software metrics have been validated theoretically and empirically as good predictors of quality factors. The object-oriented metrics software provides useful information to developers and managers about the quality and object oriented structure of the design and code, but without interpretation guidelines metrics are of little value. Our main aim to analyze the object oriented metrics is that we should be able to predict the quality attributes of system so that we are able to capture the faults & defects early in the design phase. Many object-oriented metrics proposed in literature lack a theoretical basis, while other has not yet been validated. This work describes how object-oriented metrics given by CK is useful to illustrate fault-proneness of the system. Once faults are detected, then we can easily correct them out and improve the quality and reliability of the software. In this dissertation, we have measured the bugs per class per metric with the help of well known object-oriented CK metrics.
Keywords: object-oriented design, fault prediction, attributes, coupling, cohesion, size, inheritance.
Title: Prediction of Fault-Proneness using CK Metrics
Author: Monika, Preeti Sharma
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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