Abstract
Abstract 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.
Highlights
Model-driven and model-based software development approaches propose using transformations between models from different viewpoints in order to get source code
Model Driven Architecture (MDA) suggests using a chain of model transformations, namely, from a Computation Independent Model (CIM) to a Platform Independent Model (PIM), to a Platform Specific Model (PSM) and to source code [1]
Application of Natural Language Processing (NLP) to use case scenarios is partially implemented in the IDM (Integrated Domain Modelling) toolset, where processing of a use case scenario is performed using the Stanford Parser Java Library for identifying the executors Ex and describing the functional feature D that is the verb phrase VP from the text of a step in a scenario [15], [16]
Summary
Model-driven and model-based software development approaches propose using transformations between models from different viewpoints in order to get source code. In our vision of topological functioning model driven software development (Fig. 1), the starting point is exactly text fragments in a natural language These fragments represent either information about the implemented functional characteristics of the already operating system (both manual and automated), or desired functional characteristics of the system to be built. The main TFM concept is a functional feature (FFi) that represents a system functional characteristic, e.g., a business process, a task, an action, or an activity [6]. It can be specified by a unique tuple (1)
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