In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is
proposed for regression and time series forecasting. Another novel contribution is to …

Abstract Support Vector Machine (SVM) has been widely used to build software defect
prediction models. Prior studies compared the accuracy of SVM to other machine learning …

Machine Learning approaches are good in solving problems that have less information. In
most cases, the software domain problems characterize as a process of learning that …

Quantification of parameters affecting the software quality is one of the important aspects of
research in the field of software engineering. In this paper, we present a comprehensive …

Production of high-quality software at lower cost has always been the main concern of
developers. However, due to exponential increases in size and complexity, the development …

Software is a complex entity composed in various modules with varied range of defect
occurrence possibility. Efficient and timely prediction of defect occurrence in software allows …

Nowadays, the complexity and size of software systems is proliferating. These factors have
various pros and cons. On one side where they lead to better performance and satisfactory …

Software defect prediction is an essential task during the software development Lifecycle as
it can help managers to identify the most defect-proneness modules. Thus, it can reduce the …

Faults are most likely to occur during the coding phase of software development. If, before
the testing process, we can predict parts of code that are more prone to faults, then a large …

Software defect prediction is one of the most important task during the development of
software systems in order to save developers' time and effort. Discovering defects in an early …