Understanding and Supporting Debugging Workflows in Multiverse Analysis

TL;DR

This paper introduces Multiverse Debugger, a tool designed to diagnose and fix bugs in multiverse analysis workflows, addressing key usability challenges to enhance transparency and reproducibility in statistical research.

Contribution

The work presents a novel debugging tool for multiverse analysis and provides insights into debugging workflows and challenges in this context.

Findings

Multiverse Debugger helps diagnose bugs in multiverse analysis workflows.

Qualitative study reveals difficulties in understanding multiverse composition.

Design implications for future multiverse analysis tools are discussed.

Abstract

Multiverse analysis, a paradigm for statistical analysis that considers all combinations of reasonable analysis choices in parallel, promises to improve transparency and reproducibility. Although recent tools help analysts specify multiverse analyses, they remain difficult to use in practice. In this work, we identify debugging as a key barrier due to the latency from running analyses to detecting bugs and the scale of metadata processing needed to diagnose a bug. To address these challenges, we prototype a command-line interface tool, Multiverse Debugger, which helps diagnose bugs in the multiverse and propagate fixes. In a qualitative lab study (n=13), we use Multiverse Debugger as a probe to develop a model of debugging workflows and identify specific challenges, including difficulty in understanding the multiverse's composition. We conclude with design implications for future multiverse analysis authoring systems.