Software composition analysis for vulnerability detection: An empirical study on Java projects

• Main Authors: ZHAO, Lida, CHEN, Sen, XU, Zhengzi, ZHANG, Lyuye, WU, Jiahui, SUN, Jun, LIU, Yang
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2023
• 主題: SCA; Package manager; Vulnerability detection; Software Engineering
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/9317; https://ink.library.smu.edu.sg/context/sis_research/article/10317/viewcontent/fse2023_sca.pdf
• 機構: Singapore Management University

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model which consists of Scan Modes, Scan Methods, and SCA Scope for Maven (SSM), for comprehensive assessments of the dependency resolving capabilities and effectiveness of SCA tools. Based on the Evaluation Model, we first qualitatively examined 6 SCA tools’ capabilities. Next, the accuracy of dependency and vulnerability is quantitatively evaluated with a large-scale dataset (21,130 Maven modules with 73,499 unique dependencies) under two Scan Modes (i.e., build scan and pre-build scan). The results show that most tools do not fully support SSM, which leads to compromised accuracy. For dependency detection, the average F1-score is 0.890 and 0.692 for build and pre-build respectively, and for vulnerability accuracy, the average F1-score is 0.475. However, proper support for SSM reduces dependency detection false positives by 34.24% and false negatives by 6.91%. This further leads to a reduction of 18.28% in false positives and 8.72% in false negatives in vulnerability reports.