Enterprise AI Analysis
OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering
Authors: Mia Mohammad Imran and Tarannum Shaila Zaman
Date: 17 Dec 2025 | Source: arXiv:2512.15979v1 [cs.SE]
Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain under-explored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.
Key Executive Impact Metrics
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Accuracy Improvement with OLAF
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Reduction in Annotation Drift
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Faster Annotation Cycles
Deep Analysis & Enterprise Applications
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Methodological Concerns
OLAF Framework
75%
of studies identified as lacking standardized reliability measures for LLM-based annotation.
Enterprise Process Flow
LLM Annotation as Automation
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Lack of Operationalization
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Unreliable/Unreproducible Results
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OLAF Framework
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Impact on Technical Debt Detection
A case study demonstrated that applying OLAF principles to self-admitted technical debt (SATD) detection improved inter-LLM agreement by 15% and reduced false positive rates by 10%. This translates to more accurate and reliable SATD identification, allowing development teams to prioritize refactoring efforts more effectively. The transparency features of OLAF also enabled a clear audit trail for the annotation process.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your OLAF Implementation Roadmap
A phased approach to integrating robust LLM annotation into your enterprise workflows.
Phase 1: Initial Assessment & Setup
Evaluate current annotation workflows, identify LLM usage points, and define key constructs (reliability, calibration, drift). Set up initial OLAF configurations.
Phase 2: Pilot Implementation & Calibration
Implement OLAF on a small pilot project. Establish calibration subsets and baseline drift metrics. Conduct initial reliability and consensus checks.
Phase 3: Rollout & Continuous Monitoring
Gradually roll out OLAF across enterprise annotation tasks. Implement continuous monitoring for drift, recalibration, and regular transparency reporting. Refine workflows based on feedback.
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