Requirement Formalisation Using Natural Language Processing and Machine Learning: A Systematic Review

Shekoufeh Rahimi, Kevin Charles Lano, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Improvement of software development methodologies attracts developers to automatic Requirement Formalisation (RF) in the Requirement Engineering (RE) field. The potential advantages of applying Natural Language Processing (NLP) and Machine Learning (ML) in reducing the ambiguity and incompleteness of requirements written in natural languages are reported in different studies. The goal of this paper is to survey and classify existing works on NLP and ML for RF, identifying the 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 commonly 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, primarily operating on structured and semi-structured data. This study also revealed that Deep Learning (DL) techniques 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.
Original languageEnglish
Title of host publication SCITEPRESS Digital Library
Publisher SCITEPRESS Digital Library
Number of pages8
Publication statusAccepted/In press - 22 Dec 2022
EventInternational conference on Model-Based Software and Systems Engineering - Lisbon, Lisbon, Portugal
Duration: 19 Feb 202321 Feb 2023


ConferenceInternational conference on Model-Based Software and Systems Engineering
Internet address

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