  It is becoming increasingly important in the software industry, especially
with the growing complexity of software ecosystems and the emphasis on security
and compliance for manufacturers to inventory software used on their systems. A
Software-Bill-of-Materials (SBOM) is a comprehensive inventory detailing a
software application's components and dependencies. Current approaches rely on
case-based reasoning to inconsistently identify the software components
embedded in binary files. We propose a different route, an automated method for
generating SBOMs to prevent disastrous supply-chain attacks. Remaining on the
topic of static code analysis, we interpret this problem as a semantic
similarity task wherein a transformer model can be trained to relate a product
name to corresponding version strings. Our test results are compelling,
demonstrating the model's strong performance in the zero-shot classification
task, further demonstrating the potential for use in a real-world cybersecurity
context.
