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

Source: arXiv - 2512.08910v1

Overview

The paper Exploring the Garden of Forking Paths in Empirical Software Engineering Research: A Multiverse Analysis shows that the many discretionary choices researchers make when cleaning data, defining metrics, and fitting statistical models can dramatically swing the conclusions of a study. By exhaustively re‑running every plausible analysis pipeline for a published mining‑software‑repositories (MSR) paper, the authors demonstrate that the original findings are the exception rather than the rule—raising a red flag for reproducibility in software‑engineering research.

Key Contributions

• Multiverse framework applied to SE: Introduces a systematic “multiverse analysis” (all reasonable analytical paths) to a real SE dataset, something previously limited to psychology or epidemiology.
• Empirical quantification of analytical flexibility: Identifies nine pivotal decision points, generates 3,072 distinct analysis pipelines, and measures how often each reproduces the original result (<0.2%).
• Classification model for methodological choices: Proposes a structured taxonomy that helps authors document and justify each analytical decision.
• Guidelines for robustness checks: Recommends that SE papers include multiverse‑style sensitivity analyses or at least transparent rationales for every major analytical choice.
• Open‑source tooling & reproducible artifacts: Provides scripts and a reproducible workflow that other researchers can plug into their own MSR studies.

Methodology

1. Select a representative MSR study – a published paper that used observational data and statistical modeling.
2. Map the analytical decision tree: The authors pinpointed nine “forks” (e.g., how to handle missing values, which metric to operationalize, choice of regression model, inclusion/exclusion thresholds).
3. Enumerate alternatives: For each fork they listed at least one defensible alternative (e.g., median vs. mean imputation, linear vs. logistic regression).
4. Automate the multiverse: Using a combination of R/Python scripts, they automatically generated every possible combination of choices, yielding 3,072 unique analysis pipelines.
5. Run and compare: Each pipeline was executed on the original dataset, and the resulting statistical conclusions (significance, effect direction) were recorded.
6. Assess reproducibility: The authors counted how many pipelines reproduced the published claim and examined the distribution of divergent outcomes.

Results & Findings

• Only 6 out of 3,072 pipelines (<0.2%) reproduced the original paper’s headline result.
• Majority of pipelines flipped the effect direction (e.g., a factor previously reported as “positively associated” with defect density became “negatively associated” under a different but equally reasonable preprocessing step).
• Certain forks had outsized impact: Choices about data filtering and model specification accounted for >70 % of the variability in conclusions.
• Robustness is not guaranteed by standard statistical checks: Even pipelines that passed typical diagnostics (e.g., residual analysis) could yield opposite substantive claims.

Practical Implications

• For developers building analytics tools: When embedding statistical models (e.g., defect prediction, effort estimation) into CI pipelines, be aware that preprocessing choices can change model behavior dramatically. Provide configurable, well‑documented defaults and expose the impact of each option.
• For data‑driven product teams: Treat any single “research‑grade” finding as a hypothesis rather than a hard rule. Replicate the analysis with alternative preprocessing pipelines before making engineering decisions.
• For SE researchers and conference reviewers: Expect a “multiverse summary” or a sensitivity table alongside the main results. This transparency helps reviewers assess the stability of claims and reduces the risk of publishing spurious effects.
• For tool vendors (e.g., SonarQube, CodeScene): When marketing predictive features, disclose the analytical assumptions behind the models and consider offering “robustness mode” that aggregates results across multiple plausible pipelines.

Limitations & Future Work

• Single‑study case: The multiverse was applied to one MSR paper; results may differ for other domains (e.g., controlled experiments, qualitative studies).
• Decision space bounded by author judgment: The set of alternatives, while defensible, is not exhaustive; undiscovered forks could further alter outcomes.
• Computational cost: Running thousands of pipelines can be resource‑intensive, limiting adoption for very large datasets.
• Future directions: Scaling the approach to multiple studies, integrating automated decision‑tree pruning to focus on high‑impact forks, and developing community‑maintained libraries that embed multiverse analysis into common SE research workflows.

Authors

• Nathan Cassee
• Robert Feldt

Paper Information

• arXiv ID: 2512.08910v1
• Categories: cs.SE
• Published: December 9, 2025
• PDF: Download PDF