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AI-Augmented Software Engineering: Redefining Development Workflows Through Intelligent Automation
Subject area: Science,Engineering and Technology · Area of research: Software Engineering
DOI: https://doi.org/10.64388/IREV7I10-1715636
Abstract
The rapid advancement of artificial intelligence technologies has begun to reshape the discipline of software engineering. Traditional development workflows, historically centered on manual programming, static analysis tools, and human-driven debugging processes, are increasingly being complemented by intelligent automation systems capable of assisting developers throughout the software lifecycle. AI-augmented software engineering refers to a development paradigm in which machine learning models, large language models, and intelligent analytics systems support tasks such as code generation, testing, system design, and operational optimization. This paper examines how artificial intelligence is transforming software engineering workflows through intelligent automation. The study explores the integration of AI-driven tools into development environments, including automated code generation, AI-assisted debugging, and predictive analytics for software quality management. It also analyzes the evolving relationship between human developers and intelligent development systems, emphasizing collaborative workflows that combine human expertise with machine-assisted reasoning. The research highlights architectural considerations, governance requirements, and organizational strategies necessary for implementing AI-augmented development environments. By redefining development processes through intelligent automation, AI-augmented software engineering offers the potential to significantly increase productivity, improve software quality, and accelerate technological innovation.
Keywords
AI-Augmented Development; Intelligent Software Engineering; Automated Code Generation; AI-Driven DevOps; Machine Learning for Software Engineering; Intelligent Programming Environments; Development Workflow Automation; Human-AI Collaboration.
How to cite this paper
@article{1715636,
author = {Mehmet Emin Budak},
title = {AI-Augmented Software Engineering: Redefining Development Workflows Through Intelligent Automation},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {10},
pages = {711-723},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715636.pdf},
abstract = {The rapid advancement of artificial intelligence technologies has begun to reshape the discipline of software engineering. Traditional development workflows, historically centered on manual programming, static analysis tools, and human-driven debugging processes, are increasingly being complemented by intelligent automation systems capable of assisting developers throughout the software lifecycle. AI-augmented software engineering refers to a development paradigm in which machine learning models, large language models, and intelligent analytics systems support tasks such as code generation, testing, system design, and operational optimization. This paper examines how artificial intelligence is transforming software engineering workflows through intelligent automation. The study explores the integration of AI-driven tools into development environments, including automated code generation, AI-assisted debugging, and predictive analytics for software quality management. It also analyzes the evolving relationship between human developers and intelligent development systems, emphasizing collaborative workflows that combine human expertise with machine-assisted reasoning. The research highlights architectural considerations, governance requirements, and organizational strategies necessary for implementing AI-augmented development environments. By redefining development processes through intelligent automation, AI-augmented software engineering offers the potential to significantly increase productivity, improve software quality, and accelerate technological innovation.},
keywords = {AI-Augmented Development; Intelligent Software Engineering; Automated Code Generation; AI-Driven DevOps; Machine Learning for Software Engineering; Intelligent Programming Environments; Development Workflow Automation; Human-AI Collaboration.},
month = {April},
doi = {https://doi.org/10.64388/IREV7I10-1715636}
}