A Comprehensive Review of AI-Driven Software Engineering: Challenges, Opportunities, and Future Directions - D R, Medhunhashini; Marseline, K S Jeen; U, Ramya (International Journal of Scientific Research in Computer Science and Engineering, 2025)
The integration of Artificial Intelligence (AI) into Software Engineering (SE) has significantly transformed the landscape of software development, offering potential for enhanced efficiency, automation, and innovation across various stages of the software lifecycle. This review explores the current state of AI-driven software engineering, focusing on the advancements made from 2020 to 2025. We categorize state-of-the-art research into key application areas, including code generation, bug prediction, test case generation, software maintenance, and project management, highlighting AI’s impact in automating routine tasks, improving code quality, and assisting developers in decision-making processes.AI tools such as GitHub Copilot and Codex are revolutionizing code generation by leveraging large language models to produce code snippets, entire functions, and even full programs, reducing the burden on developers. In addition, AI-driven bug prediction models are aiding developers in identifying potential issues earlier, improving defect detection and prioritization. Test case generation tools like EvoSuite and Diffblue Cover automate unit test creation, enhancing testing efficiency and ensuring better code coverage. AI also contributes to software maintenance by suggesting improvements and optimizations, thereby improving long-term code quality and sustainability. Furthermore, AI is being used in project management for sprint planning, risk prediction, and resource allocation. Despite these advancements, challenges such as explainability, data quality, tool integration, and ethical concerns remain significant. This review discusses these challenges and proposes future research directions, including human-in-the-loop systems and hybrid approaches combining symbolic reasoning with neural models. We also emphasize the need for continuous research to ensure AI becoming a reliable, ethical, and effective partner in software engineering.