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Human-AI Comparative Evaluation of Test Case Generation in Scenario-Oriented Software Testing
ID：142 View Protection：ATTENDEE Updated Time：2025-12-23 13:18:11 Hits：340 Online

Start Time：2025-12-30 12:05（Asia/Amman）

Duration：10min

Session：S9 Track 5: Emerging Trends of AI/ML » S9-1Track 5: Emerging Trends of AI/ML

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Abstract
This research evaluates the effectiveness of AI-generated test cases (using GPT-4) against test cases constructed using conventional manual testing approaches in scenario-driven software testing. Manual test cases developed by applying established black-box testing methods, while GPT-4 generated test cases through structured prompts. Three scenarios—easy, moderate, and complex—used to conduct the evaluation under equivalent conditions. The primary comparisons in the present study evaluated defect detection capability, test coverage, efficiency of execution, and scenario relevance. The results indicate that AI-generated test cases provide better coverage, are faster to generate, and more effectively detect edge case faults; notably when evaluating the complex scenario. Procedural/manual testing found to be stronger in contextual reasoning and for safety critical interpretation. Overall, this research concludes that AI-generated testing is a complement to procedural/manual testing methods not a replacement. The results support a "hybrid" testing approach for modern software testing and quality assurance.
Keywords
Artificial Intelligence,GPT-4; Software Testing; Test Case Generation; Scenario-Driven Testing; Quality Assurance
Speaker
Rawan Habarneh
Student zarqa university

Submission Author
Hamed Fawareh zarqa university
Rawan Habarneh zarqa university
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Important Date

  • Conference Date

    Dec 29

    2025

    to

    Dec 31

    2025

  • Dec 20 2025

    Draft paper submission deadline

  • Dec 31 2025

    Contribution Submission Deadline

  • Dec 31 2025

    Registration deadline

Sponsored By
United Societies of Science
Organized By
Zarqa University
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