Natural Language in Requirements Engineering for Structure Inference -- An Integrative Review

TL;DR

This paper reviews NLP tools in Requirements Engineering, revealing current limitations in automatic structure extraction and suggesting the need for new, flexible methodologies for better automation.

Contribution

It provides an integrative review of over 136 NLP tools in Requirements Engineering, highlighting gaps and proposing a new approach for structure inference.

Findings

No open source tools enable direct structure extraction

Existing solutions require supervision or input limitations

Current methods are not suitable for fully automatic application

Abstract

The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Benefits have also been identified for the area of Requirements Engineering. To evaluate what work has been done and is currently available, the paper at hand provides an integrative review regarding Natural Language Processing (NLP) tools for Requirements Engineering. This assessment was conducted to provide a foundation for future work as well as deduce insights from the stats quo. To conduct the review, the history of Requirements Engineering and NLP are described as well as an evaluation of over 136 NLP tools. To assess these tools, a set of criteria was defined. The results are that currently no open source approach exists that allows for the direct/primary extraction of information structure and even closed source solutions show limitations such as supervision or input limitations, which eliminates the possibility for fully automatic and universal application. As a results, the authors deduce that the current approaches are not applicable and a different methodology is necessary. An approach that allows for individual management of the algorithm, knowledge base, and text corpus is a possibility being pursued.