Using LLMs for inferring protocol state machines may come with certain limitations, including:
Context Window Constraints: LLMs have limited context windows, which can restrict their ability to analyze large and complex codebases in a single pass. This limitation can be addressed by segmenting the code and incrementally extracting information related to the state machine, as outlined in the PROTOCOLGPT methodology.
Accuracy and Precision: LLMs may generate inaccurate or incomplete state machines, especially when tasked with complex inference tasks. To address this, fine-tuning prompts and providing incremental guidance can help improve the accuracy and precision of the inferred state machines.
Language and Syntax Variability: Different programming languages and coding styles can pose challenges for LLMs in accurately interpreting code and inferring state machines. Developing language-specific modules and prompts within PROTOCOLGPT can help mitigate this limitation.
Training Data Quality: The effectiveness of LLMs in inferring state machines relies on the quality and diversity of the training data. Ensuring that the LLM is trained on a comprehensive corpus of protocol implementations can help enhance its performance.