: 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.
Robin Nunkesser· Proceedings of the 21st Inte...· 0 citations
: Cross-platform application frameworks promise a single codebase for multiple operating systems, but pay for this with a stack of abstraction layers—application code, third-party packages, framework runtime, platform-specific bridges, and the underlying operating system or device. When a defect manifests at this stack’s surface, fault localization is hard precisely because each layer is a plausible suspect. We argue that agent-generated native reference implementations—small vertical prototypes built directly against the platform’s native APIs with the help of coding agents—make differential, layer-bisection debugging an economically viable default tactic in cross-platform development. The contribution of this position paper is threefold: a layered fault model that makes the localization problem explicit, the proposal of native probes on demand as a concrete debugging mechanism enabled by agentic code generation, and a discussion of the conditions under which the mechanism fails (cross-layer interactions, timing-dependent behavior, probe drift) together with a research agenda for controlled empirical evaluation. The position is that agent-assisted native probes shift the cost balance of cross-platform debugging in a way that is qualitatively new: a tactic that was previously reserved for teams with full multi-platform expertise becomes available, on demand, to single-stack developers.
Robin Nunkesser· Proceedings of the 21st Inte...· 0 citations