Boosting the Performance of Alias-Aware IFDS Analysis with CFL-based Environment Transformers
This program is tentative and subject to change.
The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its conventional use of access paths for field sensitivity leads to the generation of a large number of data-flow facts. This causes scalability challenges in larger programs, limiting its practical application in extensive codebases. In response, we propose a new field-sensitive technique that reinterprets the generation of access paths as a Context Free Language (CFL) for field-sensitivity and formulates it as an IDE problem. This approach significantly reduces the number of data-flow facts generated and handled during the analysis, which is a major factor in performance degradation.
To demonstrate the effectiveness of this approach, we developed a taint analysis tool, IDEDroid, in the IFDS/IDE framework. IDEDroid outperforms FlowDroid, an established IFDS-based taint analysis tool, in the analysis of 24 major Android apps while improving its precision (guaranteed theoretically). The speed improvement ranges from $2.1\times$ to $2,368.4\times$, averaging at $222.0\times$, with precision gains reaching up to $20.0%$ (in terms of false positives reduced). This performance indicates that IDEDroid is substantially more effective in detecting information-flow leaks, making it a potentially superior tool for mobile app vetting in the market.
This program is tentative and subject to change.
Fri 25 OctDisplayed time zone: Pacific Time (US & Canada) change
11:00 - 12:20 | Static Analysis and Program Verification 4OOPSLA 2024 at IBR West Chair(s): Anders Møller Aarhus University | ||
11:00 20mTalk | A Learning-Based Approach to Static Program Slicing OOPSLA 2024 Aashish Yadavally University of Texas at Dallas, Yi Li University of Texas at Dallas, Shaohua Wang Central University of Finance and Economics, Tien N. Nguyen University of Texas at Dallas Pre-print | ||
11:20 20mTalk | Boosting the Performance of Alias-Aware IFDS Analysis with CFL-based Environment Transformers OOPSLA 2024 Haofeng Li Institute of Computing Technology at Chinese Academy of Sciences, Chenghang Shi SKLP, Institute of Computing Technology, CAS, Jie Lu SKLP, Institute of Computing Technology, CAS, Lian Li Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Jingling Xue UNSW Sydney | ||
11:40 20mTalk | The ART of Sharing Points-to Analysis: Reusing Points-to Analysis Results Safely and Efficiently OOPSLA 2024 Shashin Halalingaiah UT Austin, IIT Madras, Vijay Sundaresan IBM Canada, Daryl Maier IBM Canada, V Krishna Nandivada IIT Madras | ||
12:00 20mTalk | UniSparse: An Intermediate Language for General Sparse Format Customization OOPSLA 2024 Jie Liu Cornell University, Zhongyuan Zhao Qualcomm, Zijian Ding UCLA, Benjamin Brock Parallel Computing Lab (PCL), Intel, Hongbo Rong Intel Labs, Zhiru Zhang Cornell University, USA |