TY - GEN
T1 - NLP4ReF
T2 - 2024 IEEE Aerospace Conference, AERO 2024
AU - Peer, Jordan
AU - Mordecai, Yaniv
AU - Reich, Yoram
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We introduce Natural Language Processing for Requirement Forecasting (NLP4ReF), a model-based machine learning and natural language processing solution for enhancing the Requirements Engineering (RE) process. RE continues to face significant challenges and demands innovative approaches for process efficiency. Traditional RE methods relying on natural language struggle with incomplete, hidden, forgotten, and evolving requirements during and after the critical design review, risking project failures and setbacks. NLP4ReF tackles several key challenges: a) distinguishing between functional and non-functional requirements, b) classification of requirements by their respective system classes, and c) generation of unanticipated requirements to enhance project success. NLP4ReF employs a common natural language toolkit (NLTK) package and the recently-trending Chat-GPT. We tested NLP4ReF on PROMISE_exp, a pre-existing dataset with 1000 software requirements, and PROMISE_IoT, an enhanced dataset with 2000 software and IoT requirements. We validated NLP4ReF on a genuine IoT project. NLP4ReF swiftly generated dozens of new requirements, verified by a team of systems engineers, of which over 70% were crucial for project success. We found that GPT is superior in authentic requirement generation, while NLTK excels at requirement classification. NLP4ReF offers significant time saving, effort reduction, and improved future-proofing. Our model-based design approach provides a foundation for enhanced RE practices and future research in this domain.
AB - We introduce Natural Language Processing for Requirement Forecasting (NLP4ReF), a model-based machine learning and natural language processing solution for enhancing the Requirements Engineering (RE) process. RE continues to face significant challenges and demands innovative approaches for process efficiency. Traditional RE methods relying on natural language struggle with incomplete, hidden, forgotten, and evolving requirements during and after the critical design review, risking project failures and setbacks. NLP4ReF tackles several key challenges: a) distinguishing between functional and non-functional requirements, b) classification of requirements by their respective system classes, and c) generation of unanticipated requirements to enhance project success. NLP4ReF employs a common natural language toolkit (NLTK) package and the recently-trending Chat-GPT. We tested NLP4ReF on PROMISE_exp, a pre-existing dataset with 1000 software requirements, and PROMISE_IoT, an enhanced dataset with 2000 software and IoT requirements. We validated NLP4ReF on a genuine IoT project. NLP4ReF swiftly generated dozens of new requirements, verified by a team of systems engineers, of which over 70% were crucial for project success. We found that GPT is superior in authentic requirement generation, while NLTK excels at requirement classification. NLP4ReF offers significant time saving, effort reduction, and improved future-proofing. Our model-based design approach provides a foundation for enhanced RE practices and future research in this domain.
KW - Internet of Things
KW - Machine Learning
KW - Model-Based Systems Engineering
KW - Natural Language Processing
KW - Requirements Engineering Requirement Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85193847289&partnerID=8YFLogxK
U2 - 10.1109/AERO58975.2024.10521022
DO - 10.1109/AERO58975.2024.10521022
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85193847289
T3 - IEEE Aerospace Conference Proceedings
BT - 2024 IEEE Aerospace Conference, AERO 2024
PB - IEEE Computer Society
Y2 - 2 March 2024 through 9 March 2024
ER -