Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

Mitul Dilip Bhai Modi

doi:10.32628/CSEIT24106197

Authors

Mitul Dilip Bhai Modi Qualcomm., USA Author

DOI:

https://doi.org/10.32628/CSEIT24106197

Keywords:

Generative Artificial Intelligence, Automated Software Development, Intelligent Code Analysis, Software Quality Assurance, Development Lifecycle Automation

Abstract

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

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