RACK: A Semantic Model and Triplestore for Curation of Assurance Case Evidence

Certification of large systems requires reasoning over complex, diverse evidential datasets to determine whether its software is fit for purpose. This requires a detailed understanding of the meaning of that data, the context in which it is valid, and the uses to which it may reasonably be put. Unfortunately, current practices for assuring software safety do not scale to accommodate modern Department of Defense (DoD) systems, resulting in unfavorable behaviors such as putting off fixes to defects until the risk of not mitigating them outweighs the high cost of re-certification. In this work, we describe a novel data curation system, RACK, that addresses cost-effective, scalable curation of diverse certification evidence to facilitate the construction of an assurance case.