Leveraging Generative AI in Software Development: Advantages and Difficulties

Abstract

Integrating GenAI into the software development lifecycle represents a critical advancement for current application development practices, development and maintenance. AI tools such as ChatGPT, Copilot, GitHub and Amazon CodeWhisperer tools are used by the engineers to automate routine tasks such as generation of code segments, documentation and assistance with debugging and test case creation. These functions are also to help engineer to be more efficient and to minimize the difficulty for junior level software developers to join the development. To provide ready access to information and help during development. While on the one hand it is useful but also it has its share of problems. Dependency on AI generated code is not yet considered to be secured. These are particularly used in the most critical systems where minor error may put big safety issue. There are significant concerns about intellectual property, originality of code and data privacy. Many GenAI models rely on data from openly available resources, where copyrights and sensitive information could be incorporated, as well as concern over engineers depend on AI. This can impact the fundamental problem solving capabilities of engineers and innovation will decrease. The paper is trying to deeply analyze the pros and cons of implementing GenAI into software development, analysing current applications used in software development life cycle (SDLC), drawing upon case studies and programmer experience, examining effect on code quality, team working and project timeline.This paper offers guidance in right implementation, identifying and outlining the best practice methods when integrating AI tools into software development. Examining both the opportunities and drawbacks of GenAI allows for greater understanding of how modern tools can be best utilized within the context of academic research and enterprise IT applications.