Software Defect Prediction using ANN Algorithm

The software industry is rapidly evolving due to increasing demand and technology. Software created by humans can contain a number of errors which are known as defects. The primary responsibility of a tester is to identify these errors. Because defects can manifest at any stage during software development, it can be difficult to begin testing at an early stage. When the manager misinterprets customer needs, it can result in a defect during the system design phase. Quality assurance is essential for software, and as the demand for software applications increases, the complexity of software designs also rises, leading to a greater number of bugs. This can lead to manual detection of the errors, which takes more time to complete. The only way to improve software quality is to fix the defects. By implementing a time-saving method of software defect prediction through an enhanced Machine Learning and Artificial Neural Network techniques, this paper increase the quality of the software.