SolSEE: A Source-Level Symbolic Execution Engine for Solidity
Most of the existing smart contract symbolic execution tools perform analysis on bytecode, which loses high-level semantic information presented in source code. This makes interactive analysis tasks—such as visualization and debugging—extremely challenging, and significantly limits the tool usability. In this paper, we present SolSEE, a source-level symbolic execution engine for Solidity smart contracts. We describe the design of SolSEE, highlight its key features, and demonstrate its usages through a Web-based user interface. SolSEE demonstrates advantages over other existing source-level analysis tools in the advanced Solidity language features it supports and analysis flexibility. A demonstration video is available at: https://sites.google.com/view/solsee/.
Tue 15 NovDisplayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change
14:00 - 15:30 | |||
14:00 15mTalk | Input Invariants Research Papers Dominic Steinhöfel CISPA Helmholtz Center for Information Security, Andreas Zeller CISPA Helmholtz Center for Information Security DOI Pre-print | ||
14:15 15mTalk | Modus: A Datalog Dialect for Building Container Images Research Papers Chris Tomy University College London, Tingmao Wang University College London, Earl T. Barr University College London, Sergey Mechtaev University College London DOI | ||
14:30 15mTalk | Multi-Phase Invariant Synthesis Research Papers DOI | ||
14:45 15mTalk | Parasol: Efficient Parallel Synthesis of Large Model Spaces Research Papers DOI | ||
15:00 15mTalk | Neural Termination Analysis Research Papers Mirco Giacobbe University of Birmingham, Daniel Kroening University of Oxford, Julian Parsert University of Oxford DOI | ||
15:15 7mTalk | SolSEE: A Source-Level Symbolic Execution Engine for Solidity Demonstrations Shang-Wei Lin Nanyang Technological University, Palina Tolmach Nanyang Technological University, Singapore, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Ye Liu , Yi Li Nanyang Technological University Pre-print | ||
15:23 7mTalk | MpBP: Verifying Robustness of Neural Networks with Multi-Path Bound Propagation Demonstrations Ye Zheng Shenzhen University, Shenzhen, China, Jiaxiang Liu Shenzhen University, Xiaomu Shi Shenzhen University |