The integration of Artificial Intelligence (AI) into software engineering has significantly transformed various phases of the software development lifecycle, particularly coding, testing, and maintenance. This integration has enhanced efficiency, accuracy, and overall software quality, despite some challenges that need to be addressed. AI in Coding: AI technologies have notably advanced coding practices by automating aspects of code generation. Models trained on large datasets can generate code snippets based on natural language descriptions, which accelerates the development process. Tools like GitHub Copilot, powered by OpenAI's Codex, can provide context-aware suggestions, code completions, and optimizations directly within the developer's integrated development environment (IDE). This not only boosts productivity by reducing repetitive coding tasks but also enhances code quality by adhering to best practices and minimizing errors. AI in Testing: The testing phase has also greatly benefited from AI. Automated test case generation, driven by AI models trained on software specifications and existing test cases, has improved the thoroughness and efficiency of testing processes. AI-based tools use techniques like constrained fuzzing to explore the input space intelligently and create test cases that are more likely to uncover defects. Additionally, AI's role in defect prediction has become crucial; it analyzes historical defect data and code metrics to identify code areas most susceptible to defects, helping prioritize testing efforts and optimize resource allocation. This targeted approach ensures a more reliable final product and reduces the time required for comprehensive testing. AI in Maintenance: Software maintenance is an ongoing challenge, and AI has shown promise in automating many maintenance tasks. Automated bug fixing leverages AI to identify and repair defects without human intervention. For example, semantic code search finds similar code patterns that have exhibited defects in the past and suggests appropriate fixes. AI-driven tools also aid in software refactoring by identifying code smells and anti-patterns through static and dynamic code analysis. These tools suggest and automate improvements, such as renaming variables or reorganizing code modules, which help maintain code quality and readability. Challenges and Future Research: Despite these benefits, integrating AI into software engineering presents challenges. The quality of data used to train AI models is critical, as poor data can lead to ineffective models. Moreover, the "black box" nature of many AI models makes their decisions difficult to understand and trust, creating barriers to their widespread adoption. Seamless integration of AI tools into existing workflows is another concern that needs to be addressed. Future research is needed to explore AI's potential in the design and requirements analysis phases and develop robust, explainable AI models tailored for software engineering. Improved data quality and availability through automated preprocessing and labeling strategies are also crucial. By addressing these challenges and fostering collaborative efforts between researchers and practitioners, AI can achieve its transformative potential in software engineering, leading to more streamlined and superior development practices.