Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

TL;DR

This systematic review analyzes existing automatic UML generation approaches from natural language requirements, highlighting their limitations, benefits, and the need for standardized datasets and evaluation frameworks to advance research.

Contribution

The paper provides a comprehensive review of current methods, identifies key challenges, and offers guidelines for future improvements in automatic UML transformation from natural language.

Findings

Existing approaches face constraints like ambiguity and incompleteness.

Many methods require domain-specific knowledge or manual intervention.

A standardized dataset and evaluation framework are needed for progress.

Abstract

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches of UML generation, such as restriction on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research.