TY - GEN
T1 - Natural Language Processing to Extract Contextual Structure from Requirements
AU - Vierlboeck, Maximilian
AU - Dunbar, Daniel
AU - Nilchiani, Roshanak
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Such benefits have been identified amongst others for the area of Requirements Engineering. By assessing the Natural Language Processing for Requirement Engineering status quo and literature, a necessity for an automatic and universal approach to elicit structure from requirement and specification documents was identified. This paper outlines the first steps and results towards a modularized approach that splits the core algorithm from the text corpus as an input and underlying rule/knowledge base. This separation of functions allows for individual modification of the included parts and eases or potentially removes restrictions as well as limitations, such as input rules or the necessity for human supervision. Furthermore, contextual information and links via ontology inference can be considered that are not explicit on a textual level. The initial results of the approach show the successful extraction of structural information from requirement text, which was validated by comparing the results to human interpretations for small and public sample sets. In addition, the contextual consideration and inference via ontologies is described conceptually. At the current stage, limitations still exist regarding scalability and handling of text ambiguities, but solutions for these caveats have been developed and are being tested. Overall, the approach and results presented will be integrated and are part of a novel requirement complexity assessment framework.
AB - The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Such benefits have been identified amongst others for the area of Requirements Engineering. By assessing the Natural Language Processing for Requirement Engineering status quo and literature, a necessity for an automatic and universal approach to elicit structure from requirement and specification documents was identified. This paper outlines the first steps and results towards a modularized approach that splits the core algorithm from the text corpus as an input and underlying rule/knowledge base. This separation of functions allows for individual modification of the included parts and eases or potentially removes restrictions as well as limitations, such as input rules or the necessity for human supervision. Furthermore, contextual information and links via ontology inference can be considered that are not explicit on a textual level. The initial results of the approach show the successful extraction of structural information from requirement text, which was validated by comparing the results to human interpretations for small and public sample sets. In addition, the contextual consideration and inference via ontologies is described conceptually. At the current stage, limitations still exist regarding scalability and handling of text ambiguities, but solutions for these caveats have been developed and are being tested. Overall, the approach and results presented will be integrated and are part of a novel requirement complexity assessment framework.
KW - complexity
KW - contextual information
KW - natural language processing
KW - ontology
KW - requirements engineering
KW - structure
UR - http://www.scopus.com/inward/record.url?scp=85130789723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130789723&partnerID=8YFLogxK
U2 - 10.1109/SysCon53536.2022.9773855
DO - 10.1109/SysCon53536.2022.9773855
M3 - Conference contribution
AN - SCOPUS:85130789723
T3 - SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings
BT - SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings
T2 - 16th Annual IEEE International Systems Conference, SysCon 2022
Y2 - 25 April 2022 through 23 May 2022
ER -