OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering

• URL: http://arxiv.org/abs/2512.15979v1
• Date: Wed, 17 Dec 2025 21:24:07 GMT
• Title: OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering
• Authors: Mia Mohammad Imran, Tarannum Shaila Zaman,
• Abstract summary: Large Language Models (LLMs) are increasingly used in software engineering to automate or assist annotation tasks.<n>Existing studies often lack standardized measures for reliability, calibration, and drift.<n>We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity.
• Score: 2.74296307006009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 underexplored. 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 \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.

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