Paradigm shift on Coding Productivity Using GenAI

TL;DR

This paper explores how Generative AI tools impact software development productivity in industry, highlighting benefits for routine tasks and challenges in complex, domain-specific activities, and proposing new paradigms for effective use.

Contribution

It provides empirical insights into GenAI's productivity effects in industrial settings and introduces new paradigms for optimizing GenAI-assisted coding workflows.

Findings

GenAI improves productivity in routine coding tasks

Challenges remain in complex, domain-specific activities

Iterative prompt refinement and immersive environments are key

Abstract

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.