Multi-Dimensional Software Defect Prediction using Ensemble and Deep Learning

Software bug prediction still represents a fundamental problem in modern software engineering as system complexity grows exponentially. This paper presents a new machine learning approach for predictive bug detection in software testing. It integrates multi-dimensional features extracted from software metric to discriminate against possible defects before release and uses advanced classification algorithms (especially deep learning algorithms) for such purposes. Statistical analysis of defects suggests the superiority of our proposed SMART (Software Metrics Analysis and Risk Tracking) model in terms of the accuracy of prediction of defects, as with synthetic datasets involving 5,000 cases with twelve-dimensional features. The experimental results demonstrate marked improvement in fault detection rate while preserving computational efficiency. Filters applied within the framework implementation can compensate for existing drawbacks of real-time defect prediction approaches, thus providing practical advice to quality assurance processes. This paper serves as a building block to support automated intelligent software testing and improved quality assurance.