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The Promise and Peril of AI-Assisted Programming: Effects on Software Defects

Year: 2026

Type: article

Abstract: AI coding assistants such as GitHub Copilot are rapidly reshaping software development, but their effects on code quality remain uncertain. We analyze internal development logs from a large Chinese automobile manufacturer that adopted a Copilot-like assistant, using a difference-in-differences design to examine defect density and defect severity. Overall, AI adoption is not associated with a statistically significant reduction in defect density, but is associated with higher defect severity when defects occur. Function-level analyses further show that Q&A/chat use is associated with higher defect density and higher severity, whereas code-completion use is associated with lower defect density but higher severity. These findings advance research on human–AI collaboration and offer insights for managing AI-related quality risks in knowledge work.

Source: Journal of the Association for Information Systems

Authors: Zhang, Wei, Chen, Yuyuan, Zhang, Yueyue, Ding, Yong, Tong, Yu, Zhang, Cheng

Institutions: Geely (China), Fudan University, University of Nottingham Ningbo China, Zhejiang University

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7164784868

FWCI:

Cites: 5

Cited by:

Related to: 10