Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language

2019
Ērika Nazaruka, Jānis Osis, Viktorija Gribermane

Extracting core elements of Topological Functioning Model (TFM) from use case scenarios requires processing of both structure and natural language constructs in use case step descriptions. The processing steps are discussed in the present paper. Analysis of natural language constructs is based on outcomes provided by Stanford CoreNLP. Stanford CoreNLP is the Natural Language Processing pipeline that allows analysing text at paragraph, sentence and word levels. The proposed technique allows extracting actions, objects, results, preconditions, post-conditions and executors of the functional features, as well as cause-effect relations between them. However, accuracy of it is dependent on the used language constructs and accuracy of specification of event flows. The analysis of the results allows concluding that even use case specifications require the use of rigor, or even uniform, structure of paths and sentences as well as awareness of the possible parsing errors.

Keywords
Computation independent model, functional feature, natural language processing, Stanford CoreNLP, topological functioning model, use case.
DOI
10.2478/acss-2019-0012
Hyperlink
https://doi.org/10.2478/acss-2019-0012

Nazaruka, Ē., Osis, J., Gribermane, V. Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language. Applied Computer Systems, 2019, Vol. 24, No. 2, pp. 94-103. ISSN 2255-8683. e-ISSN 2255-8691. Available from: doi:10.2478/acss-2019-0012

Publication language
English (en)