A Configuration-First Framework for Reproducible, Low-Code Localization

Abstract:As machine learning (ML) increasingly underpins critical applications, credible, comparable, and repeatable experimental results become more important. Everyday workflows should make rigorous experiment specification and controlled execution the default while allowing advanced experimentation when required. In practice, researchers still have to combine tools for configuration, execution, versioning, and evaluation, and repeat common implementation work within their application domain. In this paper, we present a configuration-first design for application-specific ML experimentation frameworks and implement it as LOCALIZE for radio-localization research. Experiments are declared in human-readable configuration files, a workflow orchestrator executes isolated stages with explicit inputs and outputs, and code, data, configurations, environment specifications, and generated artifacts are versioned. LOCALIZE provides preconfigured datasets, processing stages, model-development procedures, and experiment templates while retaining access to the underlying pipeline. Through a qualitative comparison and controlled quantitative studies comparing LOCALIZE with corresponding Jupyter notebook and Kedro implementations, we show that, within the studied localization workflows, LOCALIZE requires fewer codebase edits for changes supported by its localization-specific functionality, while total wall-clock time and peak memory usage remain comparable. In a controlled scaling experiment covering 1x, 5x, and 10x the base dataset volume, we observed sublinear growth in total CPU and wall time over the tested sizes.

Software Engineering (cs.SE); Machine Learning (cs.LG)

arXiv:2510.25692 [cs.SE]

(or arXiv:2510.25692v4 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2510.25692 Focus to learn more arXiv-issued DOI via DataCite

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