Archana Tikayat Ray
(Advisor: Prof. Dimitri Mavris)

will propose a doctoral thesis entitled,

Natural Language Processing for Aerospace Requirements Engineering

On

Friday, March 25 at 10:00 a.m.
https://bluejeans.com/341605816/4684

Abstract
Requirements serve as the foundation for all systems, products, services, and enterprises. A well formulated requirement conveys information, which is necessary, clear, traceable, verifiable, and complete to respective stakeholders. Various types of requirements like functional, non-functional, design, quality, performance, certification requirements are used to define system functions/objectives based in the domain of interest and system being designed.

Organizations predominantly use natural language (NL) for requirements elicitation since it is easy to understand and use by stakeholders with varying levels of experience. In addition, NL lowers the barrier to entry when compared to model-based languages such as Unified Modeling Language (UML) and Systems Modeling Language (SysML), which require training. Despite the advantages, NL requirements bring along many drawbacks such as ambiguities associated with language, tedious and error-prone manual examination process, difficulties associated with verifying requirements completeness, and failure to recognize and use technical terms effectively.

Most of the systems in the present-day world are complex and warrant an integrated and holistic approach to their development to capture the numerous interrelationships. To address this need, there has been a paradigm shift towards model-centric approach to engineering as compared to traditional document-based methods. The promise shown by the model-centric approach is huge, however, the conversion of NL requirements into models is hindered by the ambiguities and inconsistencies in NL requirements. This necessitates the use of standardized/machine-readable requirements for transitioning to Model-Based Systems Engineering (MBSE).

The objective of this dissertation is to identify, develop, and implement Natural Language Processing (NLP) tools and techniques to enable/support the automated translation of NL requirements into machine-readable requirements.

Committee

• Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Prof. Daniel Schrage – School of Aerospace Engineering
• Dr. Bjorn Cole – Lockheed Martin
• Dr. Olivia Fischer – School of Aerospace Engineering