Improvement of software development methodologies attracts developers to
automatic Requirement Formalisation (RF) in the Requirement Engineering (RE)
field. The potential advantages by applying Natural Language Processing (NLP)
and Machine Learning (ML) in reducing the ambiguity and incompleteness of
requirement written in natural languages is reported in different studies. The
goal of this paper is to survey and classify existing work on NLP and ML for
RF, identifying challenges in this domain and providing promising future
research directions. To achieve this, we conducted a systematic literature
review to outline the current state-of-the-art of NLP and ML techniques in RF
by selecting 257 papers from common used libraries. The search result is
filtered by defining inclusion and exclusion criteria and 47 relevant studies
between 2012 and 2022 are selected. We found that heuristic NLP approaches are
the most common NLP techniques used for automatic RF, primary operating on
structured and semi-structured data. This study also revealed that Deep
Learning (DL) technique are not widely used, instead classical ML techniques
are predominant in the surveyed studies. More importantly, we identified the
difficulty of comparing the performance of different approaches due to the lack
of standard benchmark cases for RF.

本文基于对 257 篇文献的系统文献综述，对自然语言处理和机器学习在需求规范化中的应用进行了分类和总结，发现现有研究主要采用启发式 NLP 方法和传统机器学习技术，同时也指出了标准基准案例的缺失导致了不同方法性能比较的困难。