Type of Publication: Article in Journal

Copiloting the Future: How Generative AI Transforms Software Engineering

Author(s):

Banh, Leonardo; Holldack, Florian; Strobel, Gero

Title of Journal:

Information and Software Technology

Volume (Publication Date):

183 (2025)

pages:

107751

Keywords:

Generative AI, Software Engineering, Information System Development, Grounded Theory

Digital Object Identifier (DOI):

doi:10.1016/j.infsof.2025.107751

Citation:

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Abstract

Context

With rapid technological advancements, artificial intelligence (AI) has become integral to various sectors. Generative AI (GenAI) tools like ChatGPT or GitHub Copilot, with their unique content creation capabilities, pose transformative potential in Software Engineering by offering new ways to optimize software development processes. However, the integration into current processes also presents challenges that require a sociotechnical analysis to effectively realize GenAI's potential.

Objective

This study investigates how GenAI can be leveraged in the domain of Software Engineering, exploring its action potentials and challenges to help businesses and developers optimize the adoption of this technology in their workflows.

Method

We performed a qualitative study and collected data from expert interviews with eighteen professionals working in Software Engineering-related roles. Data analysis followed the principles of Grounded Theory to analyze how GenAI supports developers' goals, aligns with organizational practices, and facilitates integration into existing routines.

Results

The findings demonstrate several opportunities of GenAI in Software Engineering to increase productivity in development teams. However, several key barriers were also identified, that should be accounted for in successful integrations. We synthesize the results in a grounded conceptual framework for GenAI adoption in Software Engineering.

Conclusions

This study contributes to the discourse on GenAI in Software Engineering by providing a conceptual framework that aids in understanding the opportunities and challenges of GenAI. It offers practical guidelines for businesses and developers to enhance GenAI integration and lays the groundwork for future research on its impact in software development.