Software Defect Prediction Using Machine Learning Techniques

Now a days, the usage of software among the individuals have increased a lot when compared with earlier days. As the technology increasing rapidly, the evolution of artificial intelligence have been take placed. Software defect prediction in the present time of the software development life cycle stays as not only a basic but it is a significant task. From the last several days there are lot of experiments are going to detect the quality of the software which leads to give the guaranteed quality for the software. Software issue prediction indicates the probability of software shortcoming at a beginning phase of software development process and henceforth it will be more straightforward to distinguish and address them and furthermore diminish issues that would happen at later stages. This will work on the general nature of the software item. In the recent years, a few machine learning procedures which utilizes instances of flawed and non-defective modules to construct prediction models. Software metric have been utilized as input to these machine learning strategies to address the software modules. Here in this project for the detection of software we are using machine learning techniques namely Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Linear Regression

Keywords: Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Linear Regression