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.