[Remote] A Generative and Mutational Approach for Synthesizing Bug-exposing Test Cases to Guide Compiler Fuzzing
Random test case generation, or fuzzing, is a viable means for uncovering compiler bugs. Unfortunately, compiler fuzzing can be time-consuming and inefficient with purely randomly generated test cases due to the complexity of modern compilers. We present COMFUZZ, a focused compiler fuzzing framework. COMFUZZ aims to improve compiler fuzzing efficiency by focusing on testing components and language features that are likely to trigger compiler bugs. Our key insight is human developers tend to make common and repeat errors across compiler implementations; hence, we can leverage the previously reported buggy-exposing test cases of a programming language to test a new compiler implementation. To this end, COMFUZZ employs deep learning to learn a test program generator from open-source projects hosted on GitHub. With the machine-generated test programs in place, COMFUZZ then leverages a set of carefully designed mutation rules to improve the coverage and bug-exposing capabilities of the test cases. We evaluate COMFUZZ on 11 compilers for JS and Java programming languages. Within 260 hours of automated testing runs, we discovered 33 unique bugs across nine compilers, of which 29 have been confirmed and 22, including an API documentation defect, have already been fixed by the developers. We also compared COMFUZZ to eight prior fuzzers on four evaluation metrics. In a 24-hour comparative test, COMFUZZ uncovers at least 1.5× more bugs than the state-of-the-art baselines.
Wed 6 DecDisplayed time zone: Pacific Time (US & Canada) change
16:00 - 18:00 | FuzzingResearch Papers at Golden Gate C1 Chair(s): Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University | ||
16:00 15mTalk | Enhancing Coverage-guided Fuzzing via Phantom Program Research Papers Mingyuan Wu Southern University of Science and Technology and the University of Hong Kong, Kunqiu Chen Southern University of Science and Technology, Qi Luo Southern University of Science and Technology, Jiahong Xiang Southern University of Science and Technology, Ji Qi The University of Hong Kong, Junjie Chen Tianjin University, Heming Cui University of Hong Kong, Yuqun Zhang Southern University of Science and Technology Media Attached | ||
16:15 15mTalk | Co-Dependence Aware Fuzzing for Dataflow-based Big Data Analytics Research Papers Ahmad Humayun Virginia Tech, Miryung Kim University of California at Los Angeles, USA, Muhammad Ali Gulzar Virginia Tech Pre-print Media Attached | ||
16:30 15mTalk | SJFuzz: Seed & Mutator Scheduling for JVM Fuzzing Research Papers Mingyuan Wu Southern University of Science and Technology and the University of Hong Kong, Yicheng Ouyang University of Illinois at Urbana-Champaign, Minghai Lu Southern University of Science and Technology, Junjie Chen Tianjin University, Yingquan Zhao College of Intelligence and Computing, Tianjin University, Heming Cui University of Hong Kong, Guowei Yang University of Queensland, Yuqun Zhang Southern University of Science and Technology Media Attached | ||
16:45 15mTalk | Metamong: Detecting Render-update Bugs in Web Browsers through Fuzzing Research Papers Suhwan Song Seoul National University, South Korea, Byoungyoung Lee Seoul National University, South Korea Media Attached | ||
17:00 15mTalk | Property-based Fuzzing for Finding Data Manipulation Errors in Android Apps Research Papers Jingling Sun East China Normal University, Ting Su East China Normal University, Jiayi Jiang East China Normal University, Jue Wang Nanjing University, Geguang Pu East China Normal University, Zhendong Su ETH Zurich Media Attached | ||
17:15 15mTalk | Leveraging Hardware Probes and Optimizations for Accelerating Fuzz Testing of Heterogeneous Applications Research Papers Jiyuan Wang University of California at Los Angeles, Qian Zhang University of California, Riverside, Hongbo Rong Intel Labs, Guoqing Harry Xu University of California at Los Angeles, Miryung Kim University of California at Los Angeles, USA Pre-print Media Attached | ||
17:30 15mTalk | NaNofuzz: A Usable Tool for Automatic Test Generation Research Papers Matthew C. Davis Carnegie Mellon University, Sangheon Choi Rose-Hulman Institute of Technology, Sam Estep Carnegie Mellon University, Brad A. Myers Carnegie Mellon University, Joshua Sunshine Carnegie Mellon University Link to publication DOI Media Attached | ||
17:45 15mTalk | [Remote] A Generative and Mutational Approach for Synthesizing Bug-exposing Test Cases to Guide Compiler Fuzzing Research Papers Guixin Ye Northwest University, Tianmin Hu Northwest University, Zhanyong Tang Northwest University, Zhenye Fan Northwest University, Shin Hwei Tan Concordia University, Bo Zhang Tencent Security Platform Department, Wenxiang Qian Tencent Security Platform Department, Zheng Wang University of Leeds, UK Media Attached | ||