Enhancing Information Retrieval System Using Change-Prone Classes

Abstract

In today's competitive world, each company is required to change software to meet changing customer requirements. 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. This research aims to find the association between changes and object-oriented metrics using different versions of open source software. Earlier researchers have used various techniques such as statistical methods for the prediction of change-prone classes. This research uses execution time, frequency, run time information, popularity, and class dependency in prediction of change-prone classes. For evaluating the performance of the prediction model, sensitivity, specificity, and ROC curve are used. Higher values of AUC indicate the prediction model gives accurate results. 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.