Abstract

Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that 53% of journal papers, 42% of conference papers, and 5% of book chapters’ address requirements-based testing. Most of the studies use UML, activity, and use case diagrams for test case generation from requirements. One of the significant lessons learned is that most software testing errors are traced back to errors in natural language requirements. A substantial amount of work focuses on UML diagrams for test case generations, which cannot capture all the system’s developed attributes. Furthermore, there is a lack of UML-based models that can generate test cases from natural language requirements by refining them in context. Coverage criteria indicate how efficiently the testing has been performed 12.37% of studies use requirements coverage, 20% of studies cover path coverage, and 17% study basic coverage.

Details

Title
Automated Test Case Generation from Requirements: A Systematic Literature Review
Author
Ahmad, Mustafa; Wan-Kadir, Wan M N; Ibrahim, Noraini; Shah, Muhammad Arif; Younas, Muhammad; Khan, Atif; Zareei, Mahdi; Alanazi, Faisal
Pages
1819-1833
Section
REVIEW
Publication year
2021
Publication date
2021
Publisher
Tech Science Press
ISSN
1546-2218
e-ISSN
1546-2226
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2691783315
Copyright
© 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.