How to Evaluate an SCA with Reachability: Benchmarking Hard-...

Evaluating the accuracy of reachability analysis in Software Composition Analysis (SCA) tools is crucial to ensure the identification of vulnerabilities that can be exploited. This involves understanding both static and dynamic reachability analysis, with a preference for static due to its foundational benefits, and focusing on function-level reachability, which precisely identifies vulnerabilities at the function call level. The accuracy of these analyses is primarily measured by false positive and false negative rates; the latter is particularly concerning as it indicates missed vulnerabilities that are actually exploitable. Accurate assessments require stress-testing with complex code examples to simulate challenging scenarios, such as dynamic property writes and reflection, which are notoriously difficult for static analysis. Coana's reachability analysis for JavaScript and Java exemplifies an advanced approach, integrating academic research to handle difficult language features and dependencies effectively. The post emphasizes the importance of thorough benchmarking and choosing providers that offer fine-grained, function-level reachability to mitigate the risk of undetected vulnerabilities.

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