Abstract :

The Exponential Growth In Software Complexity Has Intensified The Demand For Automated Testing Methodologies That Can Efficiently Generate Comprehensive Test Cases From Natural Language Requirements. Traditional Manual Test Case Generation Is Labor-intensive, Error-prone, And Often Fails To Achieve Adequate Coverage Of Complex System Behaviors. This Paper Presents A Novel Machine Learning Framework That Leverages Transformer-based Language Models And Reinforcement Learning Techniques To Automatically Generate High-quality Test Cases Directly From Software Requirements Specifications. Our Approach Combines Natural Language Processing (NLP) With Semantic Understanding To Extract Testable Scenarios, Boundary Conditions, And Edge Cases From Unstructured Requirement Documents. We Introduce A Hybrid Architecture That Integrates BERT-based Requirement Analysis With GPTbased Test Case Synthesis, Enhanced By A Reinforcement Learning Component That Optimizes Test Case Quality Through Feedback Mechanisms. Experimental Evaluation On Five Industrial Software Projects Demonstrates That Our Approach Achieves 87.3% Requirement Coverage, 92.1% Defect Detection Rate, And Reduces Manual Test Case Creation Time By 73%. The Generated Test Cases Exhibit Superior Fault Detection Capabilities Compared To Manually Created Test Suites, With A 34% Improvement In Mutation Score. Our Contributions Include: (1) A Comprehensive Taxonomy Of Requirement-to-test Mappings, (2) A Novel ML Architecture For Automated Test Generation, (3) Extensive Empirical Validation Across Diverse Domains, And (4) Open-source Tools For Practitioners. The Results Indicate Significant Potential For Transforming Software Testing Practices Through Intelligent Automation. Keywords— Test Case Generation, Requirements Engineering, Natural Language Processing, Machine Learning, Software Testing Automation

Published:

11-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

97-102

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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