Paradigm shift on Coding Productivity Using GenAI
It addresses the problem of limited empirical evidence on GenAI's productivity in industrial settings for software engineers, though it is incremental as it builds on existing knowledge of GenAI applications.
This paper investigates the productivity effects of GenAI coding assistants in telecommunications and FinTech, finding they enhance productivity in routine tasks like refactoring but face challenges in complex, domain-specific activities due to limited context-awareness.
Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.