Natural Language Processing-Based Software Testing: A Systematic Literature Review
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...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10542730/ |
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| author | Mohamed Boukhlif Mohamed Hanine Nassim Kharmoum Atenea Ruigomez Noriega David Garcia Obeso Imran Ashraf |
| author_facet | Mohamed Boukhlif Mohamed Hanine Nassim Kharmoum Atenea Ruigomez Noriega David Garcia Obeso Imran Ashraf |
| author_sort | Mohamed Boukhlif |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-208a55ac04da4445882b4eef86a6ccfa |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-208a55ac04da4445882b4eef86a6ccfa2025-08-20T02:49:20ZengIEEEIEEE Access2169-35362024-01-0112793837940010.1109/ACCESS.2024.340775310542730Natural Language Processing-Based Software Testing: A Systematic Literature ReviewMohamed Boukhlif0https://orcid.org/0000-0001-9053-4345Mohamed Hanine1https://orcid.org/0000-0001-5981-2511Nassim Kharmoum2Atenea Ruigomez Noriega3David Garcia Obeso4https://orcid.org/0009-0007-0427-4243Imran Ashraf5https://orcid.org/0009-0002-4598-1482LTI Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, MoroccoLTI Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, MoroccoIPSS Team, Faculty of Sciences, Mohammed V University in Rabat, Rabat, MoroccoUniversidad Europea del Atlántico, Santander, SpainUniversidad Europea del Atlántico, Santander, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaNew 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.https://ieeexplore.ieee.org/document/10542730/Software testingnatural language processing (NLP)systematic reviewtest case generation |
| spellingShingle | Mohamed Boukhlif Mohamed Hanine Nassim Kharmoum Atenea Ruigomez Noriega David Garcia Obeso Imran Ashraf Natural Language Processing-Based Software Testing: A Systematic Literature Review IEEE Access Software testing natural language processing (NLP) systematic review test case generation |
| title | Natural Language Processing-Based Software Testing: A Systematic Literature Review |
| title_full | Natural Language Processing-Based Software Testing: A Systematic Literature Review |
| title_fullStr | Natural Language Processing-Based Software Testing: A Systematic Literature Review |
| title_full_unstemmed | Natural Language Processing-Based Software Testing: A Systematic Literature Review |
| title_short | Natural Language Processing-Based Software Testing: A Systematic Literature Review |
| title_sort | natural language processing based software testing a systematic literature review |
| topic | Software testing natural language processing (NLP) systematic review test case generation |
| url | https://ieeexplore.ieee.org/document/10542730/ |
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