A Transferability Study of Interpolation-Based Hardware Model Checking to Software Verification
Assuring the correctness of computing systems is fundamental to our society and economy, and formal verification is a class of techniques approaching this issue with mathematical rigor. Researchers have invented numerous algorithms to automatically prove whether a computational model, e.g., a software program or a hardware digital circuit, satisfies its specification. In the past two decades, Craig interpolation has been widely used in both hardware and software verification. Despite the similarities in the theoretical foundation between hardware and software verification, previous works usually evaluate interpolation-based algorithms on only one type of verification tasks (e.g., either circuits or programs), so the conclusions of these studies do not necessarily transfer to different types of verification tasks. To investigate the transferability of research conclusions from hardware to software, we adopt two performant approaches of interpolation-based hardware model checking, (1) Interpolation-Sequence-Based Model Checking (Vizel and Grumberg, 2009) and (2) Intertwined Forward-Backward Reachability Analysis Using Interpolants (Vizel, Grumberg, and Shoham, 2013), for software verification. We implement the algorithms proposed by the two publications in the software verifier CPAchecker because it has a software-verification adoption of the first interpolation-based algorithm for hardware model checking from 2003, which the two publications use as a comparison baseline. To assess whether the claims in the two publications transfer to software verification, we conduct an extensive experiment on the largest publicly available suite of safety-verification tasks for the programming language C. Our experimental results show that the important characteristics of the two approaches for hardware model checking are transferable to software verification, and that the cross-disciplinary algorithm adoption is beneficial, as the approaches adopted from hardware model checking were able to tackle tasks unsolvable by existing methods. This work consolidates the knowledge in hardware and software verification and provides open-source implementations to improve the understanding of the compared interpolation-based algorithms.
Thu 18 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
10:30 - 11:00 | |||
10:30 30mPoster | DyPyBench: A Benchmark of Executable Python Software Posters Islem BOUZENIA University of Stuttgart, Bajaj Piyush Krishan University of Stuttgart, Michael Pradel University of Stuttgart | ||
10:30 30mPoster | Shadows in the Interface: A Comprehensive Study on Dark Patterns Posters Liming Nie Nanyang Technological University, Yangyang Zhao Zhejiang Sci-Tech University, Chenglin Li Zhejiang Sci-Tech University, Xuqiong Luo Changsha University of Science and Technology, Yang Liu Nanyang Technological University | ||
10:30 30mPoster | Do Large Language Models Recognize Python Identifier Swaps in their Generated Code? Posters DOI Pre-print Media Attached File Attached | ||
10:30 30mPoster | Understanding Developers' Discussions and Perceptions on Non-Functional Requirements: The Case of the Spring Ecosystem Posters Anderson Oliveira Pontifical Catholic University of Rio de Janeiro (PUC-Rio), João Lucas Correia Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Wesley Assunção North Carolina State University, Juliana Alves Pereira Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rafael de Mello Federal University of Rio de Janeiro (UFRJ), Daniel Coutinho Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Caio Barbosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Paulo Vítor C. F. Libório Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Alessandro Garcia Pontifical Catholic University of Rio de Janeiro (PUC-Rio) | ||
10:30 30mPoster | ProveNFix: Temporal Property guided Program Repair Posters Yahui Song National University of Singapore, Xiang Gao Beihang University, Wenhua Li National University of Singapore, Wei-Ngan Chin National University of Singapore, Abhik Roychoudhury National University of Singapore | ||
10:30 30mPoster | PBE-based Selective Abstraction and Refinement for Efficient Property Falsification of Embedded Software Posters | ||
10:30 30mPoster | A Transferability Study of Interpolation-Based Hardware Model Checking to Software Verification Posters DOI Media Attached | ||
10:30 30mPoster | Evaluating and Improving ChatGPT for Unit Test Generation Posters Zhiqiang Yuan Fudan University, Mingwei Liu Fudan University, Shiji Ding Fudan University, Kaixin Wang Fudan University, Yixuan Chen Yale University, Xin Peng Fudan University, Yiling Lou Fudan University | ||
10:30 30mPoster | Testing AI Systems Leveraging Graph Perturbation Posters Zhaorui Yang University of California, Riverside, Haichao Zhu Tencent America, Qian Zhang University of California, Riverside | ||
10:30 30mPoster | Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning Posters Aashish Yadavally University of Texas at Dallas, Yi Li University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
10:30 30mPoster | Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example Posters Malinda Dilhara University of Colorado Boulder, Abhiram Bellur University of Colorado Boulder, Timofey Bryksin JetBrains Research, Danny Dig University of Colorado Boulder, JetBrains Research | ||
10:30 30mPoster | A Deep Dive into Large Language Models for Bug Fixing Posters Soneya Binta Hossain University of Virginia, Nan Jiang Purdue University, Qiang Zhou Amazon Web Services, Xiaopeng LI Amazon Web Services, Wen-Hao Chiang Amazon Web Services, Yingjun Lyu Amazon Web Services, Hoan Nguyen Amazon Web Services, Omer Tripp Amazon Web Services | ||
10:30 30mPoster | A Quantitative and Qualitative Evaluation of LLM-based Explainable Fault Localization Posters Sungmin Kang Korea Advanced Institute of Science and Technology, Gabin An Korea Advanced Institute of Science and Technology, Shin Yoo Korea Advanced Institute of Science and Technology | ||
10:30 30mPoster | IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion Posters Bolun Li Shandong Normal University, Zhihong Sun Shandong Normal University, Tao Huang Shandong Normal University, Hongyu Zhang Chongqing University, Yao Wan Huazhong University of Science and Technology, Chen Lyu Shandong Normal University, Ge Li Peking University, Zhi Jin Peking University |
This room is conjoined with the Foyer to provide additional space for the coffee break, and hold poster presentations throughout the event.