The Role of Artificial Intelligence in Software Engineering: A Review of Frameworks and Impact on the Software Development Life Cycle

Abstract

Computer programming Artificial intelligence is now the key that makes it possible to redesign, develop, and maintain software systems. The automation of software development life cycle (SDLC), predictive modelling, and intelligent decision assistance are all products of AI implementation in the SDLC, which has led to improved productivity and quality of products, in turn. The paper is a comprehensive evaluation of AI models and the reasons why they are relevant to the development of software engineering practice. General-purpose frameworks such as TensorFlow, PyTorch and Keras are very helpful in the creation of larger models. Domain-specific models such as Code BERT, GPT-based tools and Auto ML platforms can perform particularly significant tasks, such as code generation, defect detection and automated testing. The paper explores how the different AI methodologies have been incorporated in the SDLC considering aspects like requirements engineering, system design, implementation, testing, deployment, and maintenance. Furthermore, the trend shifts to explainable AI, intelligent maintenance, and self-managed systems have new tendencies, and it is a broader shift towards sustainable and responsible usage of AI in software engineering. Overall, the given paper sums up the most significant progress and achievements, and it can be of great value to the researchers, practitioners, and other interested parties who want to apply AI to create a new layer in the software.