AI in the Software Development Lifecycle: Insights and Open Research Questions

The rapid advancements in Artificial Intelligence (AI) and Large Language Models (LLMs) are reshaping software engineering and automating tasks such as code generation, debugging, testing, and maintenance. AI-powered tools (i.e. ChatGPT, DeepSeek), have demonstrated significant potential in enhancing developer productivity and accelerating software development processes. Integrating AI and LLMs into software engineering presents notable challenges despite these advancements. Concerns regarding the reliability of AI-generated code, security vulnerabilities, and the propagation of biases in training data pose substantial risks. Additionally, ethical considerations, including intellectual property rights, transparency, and the need for human oversight, highlight the complexities of AI adoption in critical software systems. The rapid evolution of these technologies requires continuous adaptation of software engineering methodologies to mitigate risks while maximizing benefits. This paper analyzes AI's role in software engineering, identifying key applications, challenges, and future research directions. We examine AI's impact across various phases of the software development lifecycle, This paper contributes to the ongoing discussion on AI-driven software engineering and outlines a research agenda for navigating this rapidly evolving field.