The U.S. healthcare sector is governed by strict regulatory frameworks such as HIPAA, HITECH, and the Affordable Care Act, placing immense compliance burdens on Human Resource Management (HRM) software systems. Traditional quality assurance (QA) methods often rely on manual code review and static testing, making it difficult to ensure full compliance coverage. This research proposes an NLP-based QA automation framework that bridges the gap between legal compliance texts and HRM system requirements. By leveraging Natural Language Processing (NLP) techniques, such as named entity recognition, semantic role labeling, and rule-based extraction, the framework automatically translates compliance documents into executable validation rules. These rules are then applied during the software QA process to flag deviations or potential violations early in development. The proposed model is evaluated using real-world compliance data and simulated HRM workflows, demonstrating improved accuracy, coverage, and efficiency compared to traditional methods. Our results highlight the framework’s potential to significantly reduce compliance errors, streamline QA cycles, and enhance software reliability in the healthcare domain. This study contributes a novel, domain-specific application of NLP in compliance automation and provides a foundation for further development of intelligent, regulation-aware HRM systems in healthcare.