# Large Language Models for Software Engineering: A Reproducibility Crisis

arXiv: https://arxiv.org/abs/2512.00651 · HTML: https://mlsanity.com/paper/2512.00651/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2025.

## Built on by (most cited first, 8 of 8)

- [PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents](https://mlsanity.com/paper/2606.12329/) (2026)
- [Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics](https://mlsanity.com/paper/2603.22018/) (2026)
- [Rollout Cards: A Reproducibility Standard for Agent Research](https://mlsanity.com/paper/2605.12131/) (2026)
- [Agent-Based Software Artifact Evaluation](https://mlsanity.com/paper/2602.02235/) (2026)
- [Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks](https://mlsanity.com/paper/2606.01975/) (2026)
- [The Half-Lives of Generative-AI Evidence: A 40-Record Audit, a Claim-Currency Framework, and a Reflexive Case of Frontier-Model-Assisted Research](https://mlsanity.com/paper/2607.24032/) (2026)
- [Model Retirement Creates Reproducibility Risk in Biomedical AI Publications](https://mlsanity.com/paper/2609.04699/) (2026)
- [Pass or Fail? Evaluating LLMs on Two Greek Examination Benchmarks](https://mlsanity.com/paper/2609.34800/) (2026)

## Abstract (arXiv)

Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, machine learning, and natural language processing venues, extracting structured metadata from publications, repositories, and documentation. Guided by four research questions, we examine (i) the prevalence of reproducibility smells, (ii) how reproducibility has evolved over time, (iii) whether artifact evaluation badges reliably reflect reproducibility quality, and (iv) how publication venues influence transparency practices. Using a taxonomy of seven smell categories: Code and Execution, Data, Documentation, Environment and Tooling, Versioning, Model, and Access and Legal, we manually annotated all papers and associated artifacts. Our analysis reveals persistent gaps in artifact availability, environment specification, versioning rigor, and documentation clarity, despite modest improvements in recent years and increased adoption of artifact evaluation processes at top SE venues. Notably, we find that badges often signal artifact presence but do not consistently guarantee execution fidelity or long-term reproducibility. Motivated by these findings, we provide actionable recommendations to mitigate reproducibility smells and introduce a Reproducibility Maturity Model (RMM) to move beyond binary artifact certification toward multi-dimensional, progressive evaluation of reproducibility rigor.