Ensembling LLMs for AI-Augmented Cybersecurity Software Requirements Generation
[Inżynieria oprogramowania] Autorzy: Santiago Perez-Acuna, Yod-Samuel Martín, Juan C. Yelmo
Translating high-level controls from security standards into concrete, system-specific requirements is central to cybersecurity requirements engineering. Large language models (LLMs) can accelerate this labor-intensive, recall-sensitive task, but any single run is unreliable: it misses valid safeguards while introducing plausible hallucinations, and outputs shift across runs and models. We reframe this variability as a resource: rather than selecting one output, we study post-generation ensembling, aggregating stochastic runs with information-retrieval data-fusion operators. We propose two strategies: Uniform fusion rewards mere cross-run agreement, whereas Naive-Bayes fusion weights each run by its estimated reliability. We evaluate both over 24 runs from 12 configurations across four model families, generated for ten ISO/IEC 27002:2022 controls and expert-judged against a gold standar…
- Kategoria arxiv
- cs.SE
- Kategoria
- Inżynieria oprogramowania