AI-Powered Tools in Software Engineering Applications in Code Generation and Quality Assurance

Abstract

Modern software engineering is being red financed by tools based on Artificial Intelligence (AI) and specifically in the realms of the code generation, quality assurance, and continuous integration and deployment (CI/CD). Machine learning, deep learning, natural language processing, and large language model and practice have made intelligent systems shift of traditional rule-based automation to collaborative development support. GitHub Copilot and transformer-based models introduced by Open AI are examples of tools which have shown significant advances in developer productivity, code quality and automated error detection through presented code suggestions and intelligent refactoring options. In quality assurance, AI-based methods enable automated test generation, defect prediction, and anomaly detection, which help to make the process of manual testing much easier and enhance the reliability of the software. In a similar fashion, AI-based CI/CD pipelines make use of predictive analytics and real-time performance inspection to optimize build phases, identify deployment anomalies, and improve system stability. Nonetheless, the implementation of AI in software engineering also brings about issues of data quality, interpretability of model, trust and ethics issues that should be addressed to ensure responsible deployment the increase in the significance of human AI cooperation and the attainment of scalable, dependable, and ethically appropriate inclusion of AI into modern software engineering practice.