Observatory software management in the era of AI-assisted software engineering

Abstract

Thanks to advancements in generative AI and Large Language Models (LLMs), the last five years have seen exponential growth in the adoption of AI-Assisted software engineering across many industries. Simple developer tools used for code completion, static analysis and syntax linting have been augmented by semi-autonomous agents, able to contribute to a broad set of Software Engineering practices including code generation, documentation generation, test creation, refactoring, architectural assistance, enhanced Integrated Developer Environment (IDE) tools and analysis and verification. The way we develop, analyze and test software is rapidly changing. Recent industry reports show that at least 84% of professional software developers already use AI coding assistants regularly, with 52% of developers reporting that AI tools have had a positive effect on their productivity. At GMTO, we stand to benefit from these technologies and adopting industry best practices. However, it needs to be done in a way that carefully considers our unique concerns and risks. These include code quality, maintainability and technical debt; scientific and engineering integrity, governance, trust and over-reliance; and ethical, talent and workforce issues. In this paper we describe how we are starting to utilize AI-assisted software engineering at GMTO, why these common industry concerns matter for Observatory software teams, and future plans.