Bridging the Gap Between Traditional Software Engineering and Modern AI Development Practices
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P104Keywords:
Artificial Intelligence, Software Engineering, Machine Learning, Agile DevelopmentAbstract
AI and ML have been advancing at a very fast rate and this has led to a number of challenges in the field of SE. AI-based systems are the creation of new paradigms, tools, and workflow, which are quite different from traditional SE processes. This paper discusses the differences between conventional SE and AI development to establish the gaps that separate both fields before presenting a structured approach to filling such gaps. The areas that may be addressed by the research include modifications in SDLC, the training of an AI model, practices for version control and other appropriate ethical concerns. Also, this paper provides applications where authors make an effort to introduce AIDD into traditional SE practices. Last, the future trends and recommendations for further enhancement of the integration of AI-SE are presented
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