Improved DevOps Lifecycle by Integrating a Novel Tool V-Git Lab

Abstract

we propose a tool that can automatically generate datasets for software defect prediction from GitHub repositories DevOps is a software development approach that emphasizes collaboration, communication, and automation in order to improve the speed and quality of software delivery This study aims to demonstrate the effectiveness of the tool, and in order to do so, a series of experiments were conducted on several popular GitHub repositories and compared the performance of our generated datasets with existing datasets. The tool works by analyzing the commit history of a given repository and extracting relevant features that can be used to predict defects. These features include code complexity metrics, code churn, and the number of developers involved in a particular code change. Our results show that the datasets generated by our tool are comparable in quality to existing datasets and can be used to train effective software defect prediction models Overall, the proposed tool provides a convenient and effective way to generate high-quality datasets for software defect prediction, which can significantly improve the accuracy and reliability of prediction models.