Processing Use Case Scenarios and Text in a Formal Style as Inputs for TFM-based Transformations

Abstract

TFM (Topological Functioning Model) based transformations start from text fragments as inputs and end with source code. Automated processing of use case scenarios is likely to be more predictable than text in a formal style thanks to their structure. The goal of the research is to understand whether the differences in processing these two text forms are essential for getting core elements of a TFM, or even a structured form has essential limitations. The theoretical results illustrate that use case specifications may have more structured and less structured formats. Even in the former format, use case steps may contain explanations and even text fragments in a formal style that increases unpredictability. Analysis of text in the both cases requires part-of-speech tagging, lemmas, constituency and dependency parsing, coreference resolution, and language pattern matching. Thus, structuring the initial documents is questionable but cases when they are to be managed in projects.

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

“…The text is sent to NLP tool as a whole in order to reduce time of processing. The obtained result is then processed using the following patterns [20] Rule 5. Identify pre-conditions PrCond.…”