Boosting the Performance of Multi-Solver IFDS Algorithms with Flow-Sensitivity Optimizations

The IFDS (Inter-procedural, Finite, Distributive, Subset) algorithms are popularly used to solve a wide range of analysis problems. In particular, many interesting problems are formulated as multi-solver IFDS problems which expect multiple interleaved IFDS solvers to work together. For instance, tai...

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Vydáno v:Proceedings / International Symposium on Code Generation and Optimization s. 296 - 307
Hlavní autoři: Li, Haofeng, Lu, Jie, Meng, Haining, Cao, Liqing, Li, Lian, Gao, Lin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 02.03.2024
Témata:
Boosting
Codes
multi-solver IFDS
Optimization
Scalability
taint analysis
ISSN:2643-2838
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Shrnutí:The IFDS (Inter-procedural, Finite, Distributive, Subset) algorithms are popularly used to solve a wide range of analysis problems. In particular, many interesting problems are formulated as multi-solver IFDS problems which expect multiple interleaved IFDS solvers to work together. For instance, taint analysis requires two IFDS solvers, one forward solver to propagate tainted data-flow facts, and one backward solver to solve alias relations at the same time. For such problems, large amount of additional data-flow facts need to be introduced for flow-sensitivity. This often leads to poor performance and scalability, as evident in our experiments and previous work. In this paper, we propose a novel approach to reduce the number of introduced additional data-flow facts while preserving flow-sensitivity and soundness. We have developed a new taint analysis tool, SADROID, and evaluated it on 1,228 open-source Android APPs. Evaluation results show that SADROID significantly outperforms FLowDROID (the state-of-the-art multi-solver IFDS taint analysis tool) without affecting precision and soundness: the run time performance is sped up by up to 17.89X and memory usage is optimized by up to 9X.
ISSN:2643-2838
DOI:10.1109/CGO57630.2024.10444884