![]() ![]() In particular, interfaces declare contracts that are meant to remain stable during the evolution of a software system while the implementation in concrete classes is more likely to change. ![]() While results showed strong correlations and good predictive power of these metrics, they do not distinguish between interface, abstract or concrete classes. Recent empirical studies have investigated the use of source code metrics to predict the change- and defect-proneness of source code files and classes. Results are validated in two phases: Experimental Analysis I validates results using OpenClinic software and OpenHospital software and Experimental Analysis II validates result using Neuroph 2.9.2 and Neuroph 2.6. ![]() Higher values of AUC indicate the prediction model gives accurate results. For evaluating the performance of the prediction model, sensitivity, specificity, and ROC curve are used. This research uses execution time, frequency, run time information, popularity, and class dependency in prediction of change-prone classes. Earlier researchers have used various techniques such as statistical methods for the prediction of change-prone classes. This research aims to find the association between changes and object-oriented metrics using different versions of open source software. At the same time, an efficient information retrieval system is required as changes made to software in different versions can lead to complicated retrieval systems. In today's competitive world, each company is required to change software to meet changing customer requirements.
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