Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2025

Abstract

The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative, and mixed-method approaches, are well established. However, this paradigm shift introduces novel data types and redefines many concepts in the software engineering process. The roles of developers, users, agents, and researchers increasingly overlap, blurring the distinctions between these social and technical actors within the field. This paper examines how integrating AI into software engineering challenges traditional research paradigms. It focuses on the research phenomena that we investigate, the methods and theories that we employ, the data we analyze, and the threats to validity that emerge in this new context. Through this exploration, our goal is to understand how AI adoption disrupts established software development practices that creates new opportunities for empirical software engineering research.

Keywords

Software Engineering, Generative AI, Empirical Methods.

Discipline

Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2nd ACM International Conference on AI-powered Software (AIware 2025), Seoul, South Korea, November 19-20

First Page

1

Last Page

7

Identifier

10.48550/arXiv.2502.08108

City or Country

Seoul, South Korea

Additional URL

https://doi.org/10.48550/arXiv.2502.08108

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