AI's Impact on Traditional Software Development

TL;DR

This paper explores how AI tools like GPT-4 are transforming traditional software development by automating tasks, improving efficiency, and introducing new challenges, ultimately making the process more autonomous and optimized.

Contribution

It provides a detailed analysis of integrating AI into traditional SDLC methodologies, highlighting technical advancements and associated challenges.

Findings

AI enhances code automation, testing, and debugging.

AI integration improves development speed and accuracy.

Challenges include over-dependence and ethical concerns.

Abstract

The application of artificial intelligence (AI) has brought key shifts in conventional tactical software development, including code generation, testing and debugging, and deployment. Waterfall and Agile development approaches, which have been used for a long time, also widely employ manual and well-planned steps. However, with the help of automated tools and models such as OpenAI Codex and GPT-4, many aspects of the Software Development Life Cycle (SDLC) have been made possible. This paper examines the technical aspect of integrating AI into prior traditional software development life cycle methodologies, emphasizing code automation, intelligent testing frameworks, AI-based debugging, and continuous integration and deployment pipelines. The analysis is also based on the advantages of utilizing AI for optimizations in efficiency, accuracy, and development speed alongside issues like over-dependence on AI, ethical questions, and technical constraints. Based on the case and example given in this paper, it is clearly shown that the self-improvement of AI in software development makes the process more dynamic, autonomous, and optimized.