Transforming Software Engineering Through Artificial Intelligence
Prathamesh Kamble1, Nikhil Jadhav2, Anjali Kanase3, Nikita jadhav4
1Prathamesh Kamble (MCA) ZIBACAR
2Nikhil Jadhav (MCA) ZIBACAR
3Anjali Kanase (MCA) ZIBACAR
4Nikita Jadhav (MCA) ZIBACAR
Abstract - Software engineering has evolved significantly over the past few decades, and one of the most transformative developments in this evolution is the integration of Artificial Intelligence (AI). AI technologies are reshaping traditional development workflows by automating repetitive tasks, predicting potential issues before they occur, and improving the overall quality of software.
Intelligent tools such as smart code suggestion systems, automated testing platforms, and AI-driven debugging assistants are redefining how software is designed, developed, and maintained in modern environments. With advancements in machine learning, natural language processing, and deep learning, AI systems are now capable of performing complex and higher-level tasks that were once entirely dependent on human expertise. These include understanding user requirements, generating optimized code, detecting vulnerabilities, prioritizing bugs, and even assisting in architectural decisions.
This paper explores the ongoing transformation of software engineering through AI integration. It examines the benefits such as increased productivity, improved code quality, and enhanced decision-making and also addresses the challenges related to data privacy, model reliability, ethical concerns, and the need for skilled human oversight.
Furthermore, the paper discusses emerging trends and future directions, highlighting how AI is expected to become even more deeply embedded in the software life cycle, from requirement analysis to deployment and DevOps automation.
Key Words:
Artificial Intelligence, Software Engineering, Software Development, Machine Learning, Deep Learning, Automated Coding, Code Generation, Smart Development Tools, Software Life Cycle, Predictive Analytics, Automated Testing, Requirement Analysis, AI-assisted Development, Code Optimization, Large Language Models (LLMs),
