A Nonlinear Manifold Detection based Model for Software Defect Prediction

Abstract Software defect prediction requires developing a new technique which aims at accurately predict defective modules in software system with minimum time and space complexity as well as lesser computational cost. As such, a new model based on Nonlinear Manifold Detection Techniques has been proposed to eliminate undesirable and irrelevant attributes of high dimensional datasets by dimension reduction with more prediction accuracy and improved software quality. In this paper, a novel step towards achieving the goal by developing a new model based on Nonlinear MDTs and comparing its effectiveness with existing Feature Selection techniques for identifying the most accurate defect prediction method. The performance of different classification methods with both new and existing techniques has been evaluated, compared and also tested statistically by using Friedman test and Post Hoc analysis. The result proved that new proposed model based on Nonlinear MDTs is better performance oriented compared to accuracy level of all other techniques.