NLP4ReF: Requirements Classification and Forecasting: From Model-Based Design to Large Language Models

Jordan Peer*, Yaniv Mordecai, Yoram Reich

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2024 IEEE Aerospace Conference, AERO 2024
PublisherIEEE Computer Society
ISBN (Electronic)9798350304626
DOIs
StatePublished - 2024
Event2024 IEEE Aerospace Conference, AERO 2024 - Big Sky, United States
Duration: 2 Mar 20249 Mar 2024

Publication series

NameIEEE Aerospace Conference Proceedings
ISSN (Print)1095-323X

Conference

Conference2024 IEEE Aerospace Conference, AERO 2024
Country/TerritoryUnited States
CityBig Sky
Period2/03/249/03/24

Keywords

  • Internet of Things
  • Machine Learning
  • Model-Based Systems Engineering
  • Natural Language Processing
  • Requirements Engineering Requirement Forecasting

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