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arXiv 1909.00973cs.SEcs.PL
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软件组合分析的动态性

The Dynamics of Software Composition Analysis

Darius Foo, Jason Yeo, Hao Xiao, Asankhaya Sharma

摘要:

今天的开发人员使用了大量的开源代码,这就需要自动审计和升级库依赖关系的方法,并产生了软件组合分析(SCA)的子领域。SCA产品涉及三项任务:发现依赖项、检查易受攻击代码的可达性以消除误报,以及自动修复。后两个任务依赖于应用程序和库代码的调用图来检查库中标识的特定于可扩展性的接收器是否被应用程序使用。然而,静态构造的调用图在现实世界的项目中引入了假阳性和假阴性。在本文中,我们开发了一种新的,模块化的手段相结合的调用图来自静态和动态分析,以提高性能的假阳性消除。我们的实验表明显着的性能改善。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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