Ten Simple Rules for AI-Assisted Coding in Science

TL;DR

This paper offers ten practical guidelines for using AI-assisted coding in scientific research, emphasizing maintaining scientific rigor, code quality, and human oversight to ensure reliable and valid scientific software development.

Contribution

It introduces a set of ten rules that balance AI capabilities with scientific integrity, addressing key aspects of AI-assisted coding in research contexts.

Findings

Guidelines for strategic AI use in scientific coding

Emphasis on validation and human oversight

Framework for maintaining research integrity

Abstract

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide ten practical rules for AI-assisted coding that balance leveraging capabilities of AI with maintaining scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These rules are intended to help researchers harness AI's transformative potential for faster software development while ensuring that their code meets the standards of reliability, reproducibility, and scientific validity that research integrity demands.