Evaluating Consent Requirement Satisfaction in Mobile App Designs using In-Context Learning

核心概念

Large language models can be used to verify whether mobile app designs satisfy consent requirements from the EU General Data Protection Regulation (GDPR) with high accuracy.

要約

The paper explores using in-context learning (ICL) with large language models to evaluate whether mobile app designs satisfy consent requirements from the EU General Data Protection Regulation (GDPR).

The key highlights and insights are:

1. The authors manually extracted knowledge about 8 consent requirements from GDPR guidance documents and formalized them as logical propositions.

2. They generated 400 mobile app scenarios and associated design practices, with half designed to satisfy and half to dissatisfy the consent requirements.

3. They evaluated the ability of GPT-3.5 and GPT-4 language models to verify requirement satisfaction using different prompting strategies, including a requirements-specific template, a generic template, and chain-of-thought prompting.

4. The results show that GPT-4 can verify requirement satisfaction with 95.6% accuracy and dissatisfaction with 93.2% accuracy. Chain-of-thought prompting improved GPT-3.5 performance by 9.0%.

5. The authors discuss trade-offs among the prompting strategies, model performance variations across different requirements, and the impact of scenario and requirement polarity on accuracy.

6. They also identify issues in the generated design practices, including logical inconsistencies, definition biases, and conclusory statements, which can impact the reliability of the verification process.

Overall, the study demonstrates the potential of large language models to assist in requirements engineering tasks, while highlighting the need for careful prompt engineering and dataset curation to ensure robust and reliable performance.

統計

"The overall results show that GPT-4 can be used to verify requirements satisfaction with 96.7% accuracy and dissatisfaction with 93.2% accuracy."
"Chain-of-thought prompting improves overall GPT-3.5 performance by 9.0% accuracy."

引用

"The results show that GPT-4 can be used to verify requirements satisfaction with 96.7% accuracy and dissatisfaction with 93.2% accuracy."
"Chain-of-thought prompting improves overall GPT-3.5 performance by 9.0% accuracy."

抽出されたキーインサイト

Requirements Satisfiability with In-Context Learning

by Sarah Santos... 場所 arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12576.pdf

深掘り質問

How can the identified issues in the generated design practices, such as logical inconsistencies and definition biases, be addressed to further improve the reliability of the verification process?

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.

How can the approach be extended to handle evolving regulatory guidance and legal precedents, and maintain up-to-date knowledge about requirements satisfaction?

To extend the approach to handle evolving regulatory guidance and legal precedents while maintaining up-to-date knowledge about requirements satisfaction, the following steps can be taken:

Continuous Monitoring: Regularly monitor and stay updated on changes in regulatory guidance, legal precedents, and industry standards related to requirements satisfaction. This can involve subscribing to relevant publications, attending conferences, and engaging with legal experts in the field.

Automated Updates: Implement automated systems that can ingest and process new regulatory guidance and legal precedents as they are released. This can involve setting up alerts for updates and integrating mechanisms to automatically update the knowledge base used by the language models.

Retraining Models: Periodically retrain the language models on the latest regulatory guidance and legal precedents to ensure they are up-to-date and aligned with current requirements. This retraining process can help the models adapt to changes in the regulatory landscape.

Collaboration with Legal Experts: Collaborate with legal experts and domain specialists to validate the generated design practices against the latest regulatory requirements and legal interpretations. This partnership can provide valuable insights and ensure the accuracy and relevance of the generated responses.

Version Control: Implement version control mechanisms to track changes in regulatory guidance and legal precedents over time. This can help maintain a historical record of updates and ensure that the language models are always referencing the most current information.

By incorporating these strategies, the approach can effectively handle evolving regulatory guidance and legal precedents, ensuring that the knowledge base used for requirements satisfaction remains current and accurate.

What other requirements engineering tasks, beyond consent requirement satisfaction, could be supported by large language models and in-context learning?

Large language models and in-context learning can be applied to various requirements engineering tasks beyond consent requirement satisfaction, including:

Requirement Elicitation: Language models can assist in eliciting requirements from stakeholders by generating questions, prompts, and scenarios to facilitate discussions and gather relevant information.

Requirement Analysis: Models can help analyze and prioritize requirements by identifying dependencies, conflicts, and redundancies within a set of requirements.

Requirement Traceability: Language models can support traceability by linking requirements to design artifacts, test cases, and implementation details, ensuring alignment throughout the software development lifecycle.

Requirement Validation: Models can aid in validating requirements by generating test cases, scenarios, and simulations to verify that the system meets the specified requirements.

Requirement Documentation: Language models can assist in documenting requirements by generating clear and concise descriptions, user stories, and acceptance criteria for better understanding and communication.

Requirement Evolution: Models can help manage the evolution of requirements by tracking changes, assessing impacts, and updating documentation to reflect modifications over time.

By leveraging large language models and in-context learning, these tasks can be streamlined, automated, and enhanced to improve the overall requirements engineering process in software development projects.