Natural Language Processing-Based Software Testing: A Systematic Literature Review

Boukhlif, Mohamed and Hanine, Mohamed and Kharmoum, Nassim and Ruigómez Noriega, Atenea and García Obeso, David and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, atenea.ruigomez@uneatlantico.es, david.garcia@uneatlantico.es, UNSPECIFIED (2024) Natural Language Processing-Based Software Testing: A Systematic Literature Review. IEEE Access, 12. pp. 79383-79400. ISSN 2169-3536

Text Natural_Language_Processing-Based_Software_Testing_A_Systematic_Literature_Review.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB)

Abstract

New approaches to software testing are required due to the rising complexity of today’s software applications and the rapid growth of software engineering practices. Among these methods, one that has shown promise is the introduction of Natural Language Processing (NLP) tools to software testing practices. NLP has witnessed a rise in popularity within all IT fields, especially in software engineering, where its use has improved the way we extract information from textual data. The goal of this systematic literature review (SLR) is to provide an in-depth analysis of the present body of the literature on the expanding subject of NLP-based software testing. Through a repeatable process, that takes into account the quality of the research, we examined 24 papers extracted from Web of Science and Scopus databases to extract insights about the usage of NLP techniques in the field of software testing. Requirements analysis and test case generation popped up as the most hot topics in the field. We also explored NLP techniques, software testing types, machine/deep learning algorithms, and NLP tools and frameworks used in the studied body of literature. This study also stressed some recurrent open challenges that need further work in future research such as the generalization of the NLP algorithm across domains and languages and the ambiguity in the natural language requirements. Software testing professionals and researchers can get important insights from the findings of this SLR, which will help them comprehend the advantages and challenges of using NLP in software testing.

Item Type:	Article
Uncontrolled Keywords:	Software testing, natural language processing (NLP), systematic review, test case generation
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	19 Sep 2024 08:54
Last Modified:	19 Sep 2024 08:54
URI:	http://repositorio.funiber.org/id/eprint/14279

Actions (login required)

View Item