The Dynamics of Software Composition Analysis
This work addresses the need for more accurate dependency auditing and vulnerability detection in software development, though it appears incremental as it builds on existing SCA tasks.
The paper tackles the problem of false positives and negatives in Software Composition Analysis (SCA) by developing a modular method to combine static and dynamic call graphs, resulting in significant performance improvements.
Developers today use significant amounts of open source code, surfacing the need for ways to automatically audit and upgrade library dependencies, and giving rise to the subfield of Software Composition Analysis (SCA). SCA products are concerned with three tasks: discovering dependencies, checking the reachability of vulnerable code for false positive elimination, and automated remediation. The latter two tasks rely on call graphs of application and library code to check whether vulnerability-specific sinks identified in libraries are used by applications. However, statically-constructed call graphs introduce both false positives and false negatives on real-world projects. In this paper, we develop a novel, modular means of combining call graphs derived from both static and dynamic analysis to improve the performance of false positive elimination. Our experiments indicate significant performance improvements.