AI-Augmented Research Software Engineering: A Structured Experience Report from the Development of a Python Package
TL;DR
The case suggests that generative AI is especially useful when requirements are only partially formalized, yet objective feedback from tests, benchmarks, and model quality metrics is available, and the results suggest that AI-augmented development is a relevant topic for scientific software engineering.
Abstract
: Generative AI has intensified interest in Automated Software Engineering, but most current evidence still concerns short, local coding tasks rather than the broader software engineering lifecycle. This paper presents a structured experience report on the development of a Python package for interpretable multiclass classification that hosts heterogeneous estimator families behind a shared interface. We analyze how AI assistance contributed to four recurring engineering activities—cross-language transfer, paper-to-code implementation, benchmark-driven algorithm refinement, and packaging/refactoring work—and how a small set of machine-checkable contracts acted as a cross-cutting enabler that made AI-assisted changes safer to accept. The contribution of the paper is threefold: a concrete research-software case from interpretable machine learning, a task-oriented account of where AI created practical leverage, and a set of lessons on planning, architecture, and validation for trustworthy use. The case suggests that generative AI is especially useful when requirements are only partially formalized, yet objective feedback from tests, benchmarks, and model quality metrics is available. At the same time, the study indicates that human oversight remains essential for semantic correctness, experimental validity, and maintainable software architecture. Overall, the results suggest that AI-augmented development is a relevant topic for scientific software engineering.