Leveraging Generative AI to Enhance Requirements Engineering Processes: A Systematic Literature Review

Concetti Chiave

Generative AI models, particularly large language models, have significant potential to transform and optimize various requirements engineering activities, including requirements elicitation, analysis, specification, validation, and management.

Sintesi

This systematic literature review provides a comprehensive analysis of the current state of research on the application of Generative AI (GenAI) techniques in the field of Requirements Engineering (RE). The key findings are:

1. Publication Trends:

   • There has been a significant increase in research on GenAI for RE, with the majority of publications concentrated in 2023 and 2024.
   • The utilization of preprint platforms, such as ArXiv, highlights the rapid dissemination of research findings in this fast-paced field.

2. Methodology Trends:

   • The majority of studies (51.9%) focus on enhancing the requirements elicitation phase, demonstrating the potential of GenAI in automating and improving this critical RE activity.
   • Other RE phases, such as requirements analysis (18.5%), specification (22.2%), and validation (37.0%), have also been explored, but to a lesser extent.
   • The predominant GenAI models used are large language models, particularly the GPT series, showcasing their versatility in supporting various RE tasks.
   • Researchers have prioritized addressing software quality characteristics, such as consistency, completeness, and traceability, through the application of GenAI in RE.

3. Quality Assessment:

   • The reviewed studies demonstrate a focus on rigorous evaluation, with many employing quantitative metrics, user studies, and comparative analyses to assess the performance of GenAI-based RE approaches.
   • However, challenges remain in ensuring the reliability, interpretability, and ethical implications of GenAI-generated outputs in RE.

4. Future Directions:

   • Extending GenAI applications across the entire RE lifecycle, including the underexplored areas of requirements management.
   • Enhancing domain-specific capabilities of GenAI models to better address the unique needs and constraints of various industries and application contexts.
   • Developing strategies for responsible AI integration in RE practices, addressing concerns related to bias, fairness, security, and privacy.

This systematic review highlights the critical need for comprehensive evaluation frameworks, improved human-AI collaboration models, and thorough consideration of ethical implications in GenAI-assisted RE. The insights provided can guide future research and development efforts to unlock the full potential of GenAI in revolutionizing requirements engineering practices.

Statistiche

"Only 31% of software projects are completed on time and within budget, with a mere 46% delivering high-value returns." [3]
"Frequent change requests from users and their lack of understanding of requirements are primary contributors to cost overruns." [2]

Citazioni

"The integration of AI into SE (AI for SE) has transformed traditional practices, particularly in areas such as code generation, defect prediction, and software testing, substantially improving efficiency and quality across development processes." [4, 5, 6]
"GenAI refers to a class of advanced AI systems capable of generating new content (e.g., text, images, music, and even code) on the basis of patterns learned from extensive training data." [15]
"The integration of GenAI into the RE process is shown to yield several positive outcomes, including improved requirements quality, increased efficiency, enhanced consistency, and improved traceability." [Figure 3]

Approfondimenti chiave tratti da

Generative AI for Requirements Engineering: A Systematic Literature Review

by Haowei Cheng... alle arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.06741.pdf

Domande più approfondite

How can GenAI-assisted RE practices be effectively integrated with other software engineering activities, such as design, implementation, and testing, to create a seamless and efficient software development lifecycle?

Integrating GenAI-assisted Requirements Engineering (RE) practices with other software engineering activities—such as design, implementation, and testing—can significantly enhance the overall efficiency and effectiveness of the software development lifecycle (SDLC). This integration can be achieved through several key strategies:

Cross-Functional Collaboration: Establishing a collaborative environment where requirements engineers, designers, developers, and testers work closely together is crucial. GenAI can facilitate this by providing a shared platform for real-time updates on requirements, design changes, and testing outcomes. For instance, using GenAI to generate user stories or use cases can ensure that all stakeholders have a clear understanding of requirements, which can then inform design decisions.

Automated Traceability: GenAI can enhance traceability across the SDLC by automatically linking requirements to design artifacts, code implementations, and test cases. This can be achieved through advanced techniques such as natural language processing (NLP) and pattern recognition, which can identify relationships between different artifacts. By maintaining a clear traceability matrix, teams can ensure that all requirements are addressed throughout the development process.

Feedback Loops: Implementing continuous feedback loops between RE and other phases of the SDLC is essential. GenAI can assist in gathering feedback from testing phases to refine requirements iteratively. For example, if testing reveals that certain requirements are ambiguous or unfeasible, GenAI can help in re-evaluating and refining these requirements based on stakeholder input and historical data.

Enhanced Documentation: GenAI can automate the generation of documentation across the SDLC, including requirements specifications, design documents, and test plans. By using automated documentation tools, teams can ensure that all documentation is consistent, up-to-date, and easily accessible, thereby reducing the time spent on manual documentation efforts.

Support for Agile Methodologies: In Agile environments, where requirements are expected to evolve rapidly, GenAI can provide adaptive support by generating and updating requirements dynamically based on user feedback and changing project needs. This flexibility allows for a more responsive approach to software development, ensuring that the final product aligns closely with user expectations.

By leveraging these strategies, organizations can create a seamless integration of GenAI-assisted RE practices with other software engineering activities, ultimately leading to a more efficient and effective software development lifecycle.

What are the potential risks and ethical concerns associated with the widespread adoption of GenAI in RE, and how can these be addressed to ensure responsible and trustworthy AI integration?

The widespread adoption of Generative AI (GenAI) in Requirements Engineering (RE) presents several potential risks and ethical concerns that must be addressed to ensure responsible and trustworthy AI integration:

Bias and Fairness: GenAI models can inadvertently perpetuate biases present in the training data, leading to unfair or discriminatory outcomes in requirements elicitation and analysis. To mitigate this risk, organizations should implement bias detection and correction mechanisms during the model training phase. Additionally, diverse and representative datasets should be used to train GenAI models, ensuring that they reflect a wide range of perspectives and experiences.

Transparency and Explainability: The "black box" nature of many GenAI models can hinder stakeholders' understanding of how requirements are generated or analyzed. To address this concern, developers should prioritize the creation of interpretable models and provide clear documentation on how GenAI outputs are derived. Techniques such as explainable AI (XAI) can be employed to enhance the transparency of GenAI systems, allowing stakeholders to trust and validate the AI-generated outputs.

Security and Privacy: The use of GenAI in RE may involve handling sensitive data, raising concerns about data security and privacy. Organizations must implement robust security protocols to protect sensitive information and ensure compliance with data protection regulations. Techniques such as data anonymization and encryption can help safeguard user data while still allowing for effective GenAI applications.

Accountability and Responsibility: As GenAI systems take on more responsibilities in the RE process, questions arise regarding accountability for AI-generated outputs. Establishing clear guidelines for human oversight and accountability is essential. Organizations should define roles and responsibilities for stakeholders involved in the RE process, ensuring that human experts review and validate AI-generated requirements before implementation.

Ethical Guidelines and Regulatory Frameworks: The rapid evolution of GenAI technologies necessitates the development of ethical guidelines and regulatory frameworks specific to their use in RE. Stakeholders, including researchers, practitioners, and policymakers, should collaborate to establish best practices that promote ethical AI use, ensuring that GenAI applications align with societal values and norms.

By proactively addressing these risks and ethical concerns, organizations can foster a responsible and trustworthy integration of GenAI in RE, ultimately enhancing the quality and effectiveness of software development processes.

Given the rapid advancements in natural language processing and generation, how can GenAI be leveraged to support the elicitation and analysis of requirements in emerging domains, such as the Internet of Things, edge computing, or quantum computing?

Generative AI (GenAI) can play a transformative role in supporting the elicitation and analysis of requirements in emerging domains such as the Internet of Things (IoT), edge computing, and quantum computing. Here are several ways in which GenAI can be effectively leveraged in these contexts:

Contextual Understanding: GenAI models, particularly those based on large language models (LLMs), can be trained on domain-specific data to enhance their understanding of the unique requirements and challenges associated with emerging technologies. For instance, in IoT, GenAI can analyze vast amounts of data from connected devices to identify user needs and preferences, facilitating more accurate requirements elicitation.

Automated Requirements Generation: In rapidly evolving fields like edge computing, where requirements may change frequently due to technological advancements, GenAI can automate the generation of requirements based on real-time data and user interactions. By analyzing user feedback and system performance metrics, GenAI can dynamically update requirements to reflect current needs and capabilities.

Enhanced Collaboration: GenAI can facilitate collaboration among stakeholders in emerging domains by providing a platform for interactive requirements elicitation. For example, in quantum computing, where the technical complexity may be daunting for non-experts, GenAI can assist in translating complex concepts into understandable language, enabling effective communication between technical and non-technical stakeholders.

Scenario Simulation and Analysis: GenAI can be utilized to simulate various scenarios in emerging domains, allowing stakeholders to explore potential use cases and their implications. For instance, in IoT, GenAI can generate scenarios that illustrate how different devices interact, helping to identify requirements related to interoperability, security, and user experience.

Natural Language Processing for Requirement Validation: GenAI's advanced natural language processing capabilities can be employed to validate requirements by checking for consistency, completeness, and clarity. In quantum computing, where requirements may involve intricate technical specifications, GenAI can assist in ensuring that all requirements are well-defined and aligned with project goals.

Knowledge Extraction from Literature: In rapidly evolving fields, staying updated with the latest research and technological advancements is crucial. GenAI can assist in extracting relevant information from academic papers, industry reports, and technical documentation, synthesizing this knowledge to inform the requirements elicitation process.

By leveraging these capabilities, GenAI can significantly enhance the elicitation and analysis of requirements in emerging domains, ensuring that software systems are designed to meet the unique challenges and opportunities presented by technologies such as IoT, edge computing, and quantum computing. This integration of GenAI into the requirements engineering process can lead to more innovative, efficient, and user-centered software solutions.