  Traditional software fault injection methods, while foundational, face
limitations in adequately representing real-world faults, offering
customization, and requiring significant manual effort and expertise. This
paper introduces a novel methodology that harnesses the capabilities of Large
Language Models (LLMs) augmented with Reinforcement Learning from Human
Feedback (RLHF) to overcome these challenges. The usage of RLHF emphasizes an
iterative refinement process, allowing testers to provide feedback on generated
faults, which is then used to enhance the LLM's fault generation capabilities,
ensuring the generation of fault scenarios that closely mirror actual
operational risks. This innovative methodology aims to significantly reduce the
manual effort involved in crafting fault scenarios as it allows testers to
focus on higher-level testing strategies, hence paving the way to new
possibilities for enhancing the dependability of software systems.
