Developer Productivity with GenAI

2025-10-28

AI Summary

Despite widespread adoption of generative AI (GenAI) in software development, its actual impact on developer productivity and subjective experience remains poorly understood. Method: Grounded in the SPACE framework—encompassing Satisfaction, Performance, Activity, Communication, and Efficiency—we conduct the first systematic, multi-dimensional empirical evaluation via a mixed-methods study involving 415 professional developers, combining large-scale surveys with rigorous statistical analysis. Contribution/Results: While GenAI significantly accelerates coding (increasing activity volume and efficiency), it yields no commensurate improvements in software quality, problem-solving depth, or developer satisfaction—limiting net productivity gains. Crucially, we identify a nonlinear relationship between usage frequency and outcomes: high-frequency users do not consistently achieve higher performance or satisfaction, revealing a “productivity paradox” in AI-augmented development. These findings provide both theoretical grounding and empirical evidence to guide rational assessment and optimization of GenAI-assisted software engineering practices.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generative AI (GenAI) tools are increasingly being adopted in software development as productivity aids. However, evidence regarding where and when these tools actually enhance productivity is unclear. In this paper, we investigate how GenAI adoption affects different dimensions of developer productivity. We surveyed 415 software practitioners to capture their perceptions of productivity changes associated with AI-assisted development using the SPACE framework - Satisfaction and well-being, Performance, Activity, Communication and collaboration, and Efficiency and flow. Our results, disaggregated by frequency of AI usage, reveal limited overall productivity change, highlighting the productivity paradox in which developers become faster but do not necessarily create better software or feel more fulfilled.

Problem

• Investigating GenAI's impact on developer productivity dimensions
• Measuring productivity changes using SPACE framework survey
• Identifying productivity paradox in AI-assisted software development

Innovation

• Surveyed developers using SPACE framework dimensions
• Measured productivity changes from AI-assisted development
• Analyzed limited productivity gains across usage frequencies