To address the identified issues in the generated design practices and enhance the reliability of the verification process, several strategies can be implemented:
Enhanced Training Data: Improve the training data used to generate design practices by incorporating a wider range of examples and scenarios. This can help the model learn to generate more diverse and accurate responses.
Fine-tuning Models: Fine-tune the language models on specific requirements and domain knowledge to improve their understanding and generation of design practices. This can help reduce logical inconsistencies and biases in the responses.
Human Oversight: Implement a human oversight mechanism to review and validate the generated design practices. Human experts can identify and correct any logical inconsistencies, biases, or inaccuracies in the responses.
Iterative Refinement: Continuously iterate on the generation process, incorporating feedback from human reviewers to refine the model's output. This iterative approach can help improve the quality and reliability of the generated design practices over time.
Incorporating Context: Ensure that the language models consider the context provided in the requirements and domain knowledge to generate more contextually relevant design practices. This can help reduce logical inconsistencies and improve the overall reliability of the verification process.
By implementing these strategies, the identified issues in the generated design practices can be effectively addressed, leading to a more reliable verification process for requirements satisfaction.