AI Tools in Software Development: Developer Perceptions and Usage Patterns

TL;DR

This study empirically examines how professional developers perceive and use Generative AI tools in software development, revealing positive associations between usage, perceived productivity, and quality, with distinct developer segments identified.

Contribution

It provides new empirical insights into developer perceptions, usage patterns, and segmentation related to AI tools in software development, highlighting factors influencing adoption.

Findings

Higher AI tool usage correlates with increased perceived productivity.

Developers report simultaneous improvements in speed and quality.

Three developer segments (Enthusiasts, Pragmatists, Cautious) differ in usage and perceptions.

Abstract

The use of Generative AI (GenAI) tools in software development has raised questions about their impact on productivity, code quality, and developer practices. Prior research presents mixed findings, with objective analyses identifying potential declines in code quality, while survey-based studies report perceived improvements in productivity and minimal quality trade-offs. This study presents an empirical analysis of survey data from 147 professional developers, examining associations between AI tool usage, perceived productivity, perceived code quality, and adoption intent. The results indicate that higher frequency and broader use of AI tools are associated with higher perceived productivity and perceived code quality. In contrast to concerns about a trade-off between speed and quality, developers report that these outcomes co-occur. Adoption intent is positively associated with current usage patterns and ease of integration, while security concerns show a modest negative association. AI tool usage in testing is less extensive than in coding, and perceived productivity gains are correspondingly lower. Clustering analysis identifies three developer segments-Enthusiasts, Pragmatists, and Cautious-which differ in usage breadth, perceived outcomes, and adoption intent. These patterns are consistent with diffusion-like models of technology adoption and suggest a reinforcing relationship between usage, perceived outcomes, and intended future use. These segments are also associated with differences in reported organizational context, including the presence of AI-related policies. Overall, the findings suggest that perceptions of productivity and quality, along with usage experience and integration factors, are associated with adoption behavior, highlighting the importance of considering both perceived and objective outcomes in evaluating AI-assisted software development.