"AI-Augmented Software Development: Enhancing Code Quality and Developer Productivity with Machine Learning"

Authors

  • Vinod Veeramachaneni

Keywords:

Artificial Intelligence, Machine Learning, AI integration & software engineering

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into software development has transformed how developers write, test, and optimize code. AI-driven tools enhance code quality by automating debugging, refactoring, and providing intelligent
recommendations, thereby improving developer productivity. This paper explores AI augmented software development, its impact on software engineering practices, and the effectiveness of ML models in code analysis.

References

Johnson, R., & Li, X. (2017). AI-assisted code generation: Impact on software development efficiency. Journal of Software Engineering, 34(2), 112-126.

Singh, A., Kumar, P., & Sharma, R. (2018). Deep learning for automated programming assistance. IEEE Transactions on Software Engineering, 44(5), 890-905.

Kim, D., & Chen, H. (2019). Machine learning-powered debugging tools for software reliability. ACM Computing Surveys, 51(4), 1-23.

Downloads

Published

2023-12-02

How to Cite

Vinod Veeramachaneni. (2023). "AI-Augmented Software Development: Enhancing Code Quality and Developer Productivity with Machine Learning" . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 910–917. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1847

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.