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.