Description

Software development and the maintenance life cycle are lengthy processes. However, the possibility of having defects in the software can be high. Software reliability and performance are essential measures of software success, which affects user satisfaction and software cost. Predicting software defects using machine learning (ML) algorithms is one approach in this direction. Implementing this approach in the earlier stages of the software development improves software performance quality and reduces software maintenance cost. Different models and techniques have been implemented in many studies to predict software defects. Software evolution is essential, so the software is modified over time to adapt it to changing customer and market requirements. The system is developed the different machine and deep learning algorithms such as artificial neural networks, convolutional neural network, logistic regression and support vector machine. Then, we can detect or predict the software defects or not. Software defect prediction is a necessary process that should be considered by the software developer before the software deployment. Software defect prediction can be implemented using ML-based prediction model. The model can use historical data for predicting future software defects.