Why Different SCA Tools Produce Different Results

Different Software Composition Analysis (SCA) tools can yield varying results when scanning the same project due to differences in scanning techniques, data sources, and whether a tool is built in-house or sourced from open-source providers. Scanning techniques such as package manifest scanning, semantic analysis, checksum-based scanning, source code scanning, and dependency graph scanning each offer unique strengths and weaknesses, impacting the detection of vulnerabilities and dependencies. The data and information sources that tools rely on, including timeliness, sources, and quality of vulnerability and license data, play a crucial role in the results. Additionally, the choice between building a custom scanning engine and using an open-source one involves trade-offs in terms of cost, accuracy, and capabilities. Effective use of SCA tools involves understanding the supported programming languages, strategically placing the tool within the software development lifecycle (SDLC), and setting up policies to prioritize actionable results, considering factors like fixability and vulnerability reachability.

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