# Paradigm shift on Coding Productivity Using GenAI

arXiv: https://arxiv.org/abs/2504.18404 · HTML: https://mlsanity.com/paper/2504.18404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [The Role of Generative AI in Software Development Productivity: A Pilot Case Study](https://mlsanity.com/paper/2406.00560/) (2024)
- [Assessing AI-Based Code Assistants in Method Generation Tasks](https://mlsanity.com/paper/2402.09022/) (2024)

## Abstract (arXiv)

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.