Employing NLP Approach for Formulation of Acceptance Tests based on Extracting Conditional Expressions from Requirements

Document Type : Original research papers

Authors

1 Computer and Control Engineering, Faculty of Engineering, Tanta University, Egypt

2 Department 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.

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Delta University Scientific Journal
Volume 7, Issue 3
Special Issue DU- IECRI 2024 Second International Engineering Conference on Research and Innovation Faculty of Engineering, Delta University, Egypt
November 2024
Pages 363-371

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