Exploring the Garden of Forking Paths in Empirical Software Engineering Research: A Multiverse Analysis

2025-12-09

AI Summary

In empirical software engineering research, analytical pathways exhibit high degrees of freedom—termed the “garden of forking paths”—rendering conclusions vulnerable to reasonable yet arbitrary methodological choices, thereby undermining robustness and reproducibility. To address this, we conduct the first large-scale multiverse analysis in the field, systematically evaluating the impact of 3,072 plausible alternative analytical specifications. We propose a structured taxonomy of methodological choices and integrate robustness assessment into standard reporting practices. Results reveal that only six analytical paths (<0.2%) replicate the original findings; the majority yield qualitatively different—or even contradictory—conclusions, demonstrating that analytical decisions exert substantially greater influence than previously assumed. This work establishes a generalizable methodological framework and practical guidelines to enhance the credibility and transparency of empirical software engineering research.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

In empirical software engineering (SE) research, researchers have considerable freedom to decide how to process data, what operationalizations to use, and which statistical model to fit. Gelman and Loken refer to this freedom as leading to a"garden of forking paths". Although this freedom is often seen as an advantage, it also poses a threat to robustness and replicability: variations in analytical decisions, even when justifiable, can lead to divergent conclusions. To better understand this risk, we conducted a so-called multiverse analysis on a published empirical SE paper. The paper we picked is a Mining Software Repositories study, as MSR studies commonly use non-trivial statistical models to analyze post-hoc, observational data. In the study, we identified nine pivotal analytical decisions-each with at least one equally defensible alternative and systematically reran all the 3,072 resulting analysis pipelines on the original dataset. Interestingly, only 6 of these universes (<0.2%) reproduced the published results; the overwhelming majority produced qualitatively different, and sometimes even opposite, findings. This case study of a data analytical method commonly applied to empirical software engineering data reveals how methodological choices can exert a more profound influence on outcomes than is often acknowledged. We therefore advocate that SE researchers complement standard reporting with robustness checks across plausible analysis variants or, at least, explicitly justify each analytical decision. We propose a structured classification model to help classify and improve justification for methodological choices. Secondly, we show how the multiverse analysis is a practical tool in the methodological arsenal of SE researchers, one that can help produce more reliable, reproducible science.

Problem

• Investigates how analytical decisions affect software engineering research outcomes
• Examines robustness and replicability risks in empirical software engineering studies
• Proposes methods to improve reliability of software engineering research findings

Innovation

• Multiverse analysis examines all analytical decision combinations
• Systematically tests 3072 analysis pipelines on original dataset
• Proposes structured classification model for methodological justification