Intelligent requirement-to-test-case traceability system via Natural Language Processing and Machine Learning

Accurate mapping of software requirements to tests is critical for ensuring high software reliability. However, the dynamic nature of software requirements throughout various mission phases necessitates the maintenance of traceable and measurable requirements throughout the entire mission life cycle. During the development phase, a predictable and controlled deployment, testing, and integration of software systems can strongly support a mission's rapid innovation. Similarly, during the operation phase, timely application of patches and efficient evaluation and verification processes are vital. To address these challenges, we propose a novel method that combines Natural Language Processing (NLP) and Machine Learning (ML) to automate software requirement-to-test mapping. This method formalizes the process of reviewing the recommendations generated by the automated system, enabling engineers to improve software reliability, and reduce cost and development time.