Enterprise AI Analysis
Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies
Large Language Models (LLMs) have garnered immense interest and funding, yet their non-deterministic nature and rapid evolution pose significant challenges to the reproducibility of empirical software engineering studies. This analysis investigates the current state of reproducibility and the factors that impede it, offering crucial insights for the future of AI research and implementation.
Key Findings & Enterprise Impact
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Initial Reproducibility Fit
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Fully Reproduced Results
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ACM Badge Reliability Gap
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Studies Using OpenAI Models
Deep Analysis & Enterprise Applications
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RQ1: Reproducibility Rate
RQ2: Impeding Factors
RQ3: ACM Badges
7.7%
Studies fit for reproduction
Our analysis of 65 LLM-centric empirical studies using OpenAI services found that only 5 were even fit for an attempt at reproduction (7.7%). Of these 5, none could be fully reproduced, and only 2 were partially reproducible. This highlights a critical challenge for the reliability and scientific value of current research in this rapidly evolving field.
The challenges to reproducibility stem from both general software engineering research issues and LLM-specific factors. Common problems include missing or incomplete artifacts, unspecified dependency versions, and the use of deprecated commercial LLMs. Many studies also lack crucial details about model configurations like temperature and prompt engineering.
| Factor | Overall Challenges (86 Articles) | Reproduction Challenges (18 Attempted Articles) |
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| Missing/Incomplete Artefacts |
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| Model & Config Issues |
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| Technical/Documentation Issues |
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ACM Badge Reliability Under Scrutiny
Our findings reveal that ACM artefact badges, intended to signal the reliability and reusability of research artifacts, are not a reliable indicator for LLM-centric studies. Specifically, half of the 'Artifacts Evaluated - Reusable v1.1' badges awarded for ICSE and ASE 2024 papers did not meet ACM's own requirements just one year later. Issues included incomplete or non-functional artifacts and insufficient documentation. This underscores a need for stricter, more standardized evaluation processes and potentially longer-term validity checks for artifacts in fast-evolving fields like LLM research.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic approach to integrating AI, ensuring reproducibility and long-term value in your enterprise.
Reproducibility Audit & Gap Analysis
Assess existing LLM-centric projects for reproducibility, identify missing artifacts, documentation gaps, and dependency issues. Develop a clear plan for standardizing research practices. (3-5 weeks)
Standardized Artifact Development
Implement robust artifact development guidelines, including comprehensive code, data, model versions, and detailed prompt engineering. Utilize containerization for platform independence. (6-8 weeks)
Robust Study Design & Validation
Adopt study designs that account for LLM non-determinism, define clear evaluation metrics, and incorporate independent replication checks. Integrate Bayesian bootstrapping for uncertainty quantification. (8-12 weeks)
Continuous Monitoring & Adaptation
Establish processes for tracking model updates, managing deprecated APIs, and adapting research methodologies to the evolving LLM landscape. Ensure long-term accessibility and reusability of all research outputs. (Ongoing)
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