A software defect learning and analysis utilizing regression method for quality software development

Abstract

The program is a complex object consisting of different units with variable degrees of defects. By predicting the effectiveness and frequency of program defects, program managers can make better use of manpower, cost, and time to obtain better quality assurance. It is always possible to have a set of defects that affect designed and predictable units in order to have close association with the subsidiaries. Most of the current defect prediction rating mechanism is derived from learning the previous project data, but it is not sufficient to predict the defect of the new project because the new design may contain a different type of parameter. This paper proposes a Software Defect Learning and Analysis utilizing Regression Method (SDLA-RM) to detect defects and plan a better maintenance strategy, which can support the prediction of a defective or nondefective software unit prior to deployment in any project programs. The SDL-RM mechanism extends Regression Analysis (RAM) to create an effective rulebased model for accurately classifying program faults. This approach improves the predictability of software defects, allowing software development to spend more time testing components that are expected to contain errors. The experimental evaluation is carried out across the NASA-PROMISE repository data sets, that outcome of the results in comparison with existing classifiers suggest the effectiveness and practical perspective in the software development.