LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments
[Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because reliable annotations require multiple domain experts. Large language models (LLMs) seem ideal candidates to replace human annotators. [Problem:] Coding security-specific aspects (code identifiers mentioned, lines-of-code mentioned, security keywords mentioned) may require deeper contextual understanding than sentiment classification. [Objective:] Explore whether LLMs can act as automated annotators for technical security comments by human subjects. [Method:] We prompt four top-performing LLMs on LiveBench to detect nine security-relevant cod...
| Authors | Maria Camporese, Fabio Massacci, Yuanjun Gong |
|---|---|
| Primary category | cs.SE |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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