The current prompt optimization approaches, PromSec and SafeCoder, face several limitations regarding the preservation of functionality and usability in the generated code. One significant issue is that the iterative process of optimizing prompts can lead to information loss, resulting in code that may not function as intended. This is particularly evident in the findings that approximately 98% of the generated code exhibits partially or fully deviated functionality. Such deviations can undermine the usability of the code, making it less reliable for developers who depend on accurate and functional outputs.
To address these limitations, SGCode can be improved by implementing a feedback loop mechanism that allows users to provide input on the functionality of the generated code. This could involve integrating utility tests that automatically assess the code's performance against predefined criteria. By incorporating user feedback and utility testing, SGCode can refine its prompt optimization strategies to better balance security and functionality.
Moreover, enhancing the training datasets for the underlying models to include a broader range of functional code examples could help mitigate the risk of functionality loss. This would ensure that the models are better equipped to generate code that not only meets security standards but also adheres to functional requirements. Additionally, SGCode could explore hybrid approaches that combine the strengths of both PromSec and SafeCoder, allowing for a more comprehensive optimization strategy that prioritizes both security and usability.