Presenter: Anusch Bachofer from EMPA
Research software is increasingly built by domain experts who are not trained software engineers. This talk shares a first-hand account of how a self-described programming beginner developed a production-oriented Supplier Rating Software (SRS) web application for a research department at EMPA, with an AI coding assistant (Claude) as a constant collaborator.
The project began as a project work in the ZHAW CAS Object-Oriented Programming course, carried out with a three-person student team, and has since matured into actively maintained software integrated with openBIS, the laboratory information system used across our labs.
The stack spans a Java 25 / Spring Boot backend, an Angular frontend, and a GitLab CI/CD and ArgoCD deployment pipeline — a breadth that would be daunting for a newcomer without support.
I will show concretely how AI assistance changed the learning and engineering process: not by writing code for me, but by explaining why, enforcing good practices (object-oriented design principles, ESLint, meaningful HTTP error handling), and letting me review and understand every change before applying it.
I will also discuss the limits and risks I encountered — over-reliance, verification, and keeping ownership of the codebase. The talk aims to give fellow RSEs, and especially researchers who code, an honest, practical perspective on where AI assistants genuinely accelerate research software development, and where human judgment remains essential.