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

Accurate mapping of software requirements-to-tests can assure high software reliability as a result of robust traceability, test coverage, and improved transparency. Software requirements change frequently across mission phases. A testable and measurable requirement maintenance and tracing are essential in all phases of mission life cycle. In a development phase, a predictable and controlled software system deployment, test, and integration can firmly support mission’s rapid innovations. In an operation phase, patches need to be applied periodically and prompt evaluation and verification turnaround are critical. By integrating and exercising Natural Language Processing (NLP) and Machine Learning (ML) assisted model-based test engineering (MBTE) many of these challenges can be overcome. This paper will present a new and novel method and lesson learned from formalizing and automating the software requirement-to-test mapping, which allows engineers to review the recommendations generated by the automated system.