标题： 从需求到测试用例：基于自然语言处理的高性能电子控制单元测试用例自动化方法 显示英文标题

标题： From Requirements to Test Cases: An NLP-Based Approach for High-Performance ECU Test Case Automation

摘要： 自动化测试用例规范生成对于提高软件测试的效率和准确性至关重要，特别是在像高性能电子控制单元（ECU）这样的复杂系统中。 本研究调查了使用自然语言处理（NLP）技术，包括基于规则的信息抽取和命名实体识别（NER），将自然语言需求转换为结构化的测试用例规范的方法。 使用了来自Polarion工具的400个特性元素文档的数据集来评估两种提取关键元素（如信号名称和值）的方法。 结果显示，基于规则的方法优于NER方法，达到了95\%的准确率。而利用SVM和其他机器学习算法的NER方法，达到了77.3\%的准确率。确认了与手动方法相比，基于规则的方法显著提高了效率和准确性。 本研究突显了NLP驱动的自动化在提高质量保证、减少人工努力和加速测试用例生成方面的潜力，未来的工作将集中在完善NER和混合模型以应对更大的复杂性。 显示英文摘要

摘要： Automating test case specification generation is vital for improving the efficiency and accuracy of software testing, particularly in complex systems like high-performance Electronic Control Units (ECUs). This study investigates the use of Natural Language Processing (NLP) techniques, including Rule-Based Information Extraction and Named Entity Recognition (NER), to transform natural language requirements into structured test case specifications. A dataset of 400 feature element documents from the Polarion tool was used to evaluate both approaches for extracting key elements such as signal names and values. The results reveal that the Rule-Based method outperforms the NER method, achieving 95% accuracy for more straightforward requirements with single signals, while the NER method, leveraging SVM and other machine learning algorithms, achieved 77.3% accuracy but struggled with complex scenarios. Statistical analysis confirmed that the Rule-Based approach significantly enhances efficiency and accuracy compared to manual methods. This research highlights the potential of NLP-driven automation in improving quality assurance, reducing manual effort, and expediting test case generation, with future work focused on refining NER and hybrid models to handle greater complexity.