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Data imbalancing is becoming a common problem to tackle in different fields like, defect prediction, change prediction, oil spills, medical diagnose etc. Various methods have been developed to handle imbalanced datasets in order to improve accuracy of the prediction models. Many studies have been carried out in the field of defect prediction for imbalanced datasets but most of them uses SMOTE oversampling...
The use of the Internet has become an integral part of everyone's life. Due to this, the introduction of virus and other malicious crackers is increasing everyday. This in turn leads to the introduction of defects which adversely affect the security. Thus, protecting vital information in this cyber world is not an easy task. We need to deal with security related defects to ensure failure free and...
Change in a software is crucial to incorporate defect correction and continuous evolution of requirements and technology. Thus, development of quality models to predict the change proneness attribute of a software is important to effectively utilize and plan the finite resources during maintenance and testing phase of a software. In the current scenario, a variety of techniques like the statistical...
Predicting the changes in the next release of software, during the early phases of software development is gaining wide importance. Such a prediction helps in allocating the resources appropriately and thus, reduces costs associated with software maintenance. But predicting the changes using the historical data (data of past releases) of the software is not always possible due to unavailability of...
Managing change in the early stages of a software development life cycle is an effective strategy for developing a good quality software at low costs. In order to manage change, we use software quality models which can efficiently predict change prone classes and hence guide developers in appropriate distribution of limited resources. This study examines the effectiveness of ten machine learning algorithms...
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