Employing NLP Approach for Formulation of Acceptance Tests based on Extracting Conditional Expressions from Requirements | ||||
| Delta University Scientific Journal | ||||
| Volume 7, Issue 3, November 2024, Page 363-371 PDF (747.93 K) | ||||
| Document Type: Original research papers | ||||
| DOI: 10.21608/dusj.2024.433479 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Amany M Sarhan  1; Mostafa Mohamed Ragab 2
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| 1Computer and Control Engineering, Faculty of Engineering, Tanta University, Egypt | ||||
| 2Department of Computer and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt | ||||
| Abstract | ||||
| In the continually changing industry of software engineering, assuring quality and dependability is critical. As systems evolve, requirements analysis and test case production get more complicated, making high coverage difficult to achieve. This paper describes a current way to automatically create acceptance tests that uses Natural Language Processing (NLP) to extract conditional statements from textual requirements. These conditionals are the foundation of test scenarios, and automating their extraction considerably saves the time and mistakes involved with human test case generation. CiRA (Conditionals in Requirements Artifacts), a tool-supported technique, tackles this issue by automatically producing test cases based on conditionals in natural language requirements. CiRA delivers a substantial level of automation in real circumstances through the use of NLP methods. This paper describes a case study with three industry partners—Allianz, Ericsson, and Kostal—in which CiRA successfully created more than 70% of the needed test cases. CiRA also found and developed test cases that were missed during the human test design process, indicating its efficacy in improving the reliability and completeness of acceptance testing. This technique not only speeds up the testing process, but it also provides a greater degree of system quality by including more situations with less manual involvement. | ||||
| Keywords | ||||
| Acceptance testing; Automatic test case creation; Requirements engineering; Natural language processing | ||||
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