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Investigating the Use of Large Language Models for Generating Abuser Stories for Early Security Threat Identification

This repository contains the complete dataset, experimental inputs, raw outputs, statistical scripts, and validation artifacts for the study assessing the effectiveness of Large Language Models (LLMs) in generating abuser stories from user stories under a constrained-context baseline. The study evaluated three lightweight models (GPT-4o mini, Claude 3.5 Haiku, Gemini 1.5 Flash) using two prompting techniques (Zero-Shot and One-Shot) across 10 real-world user stories.