JVM fuzzing techniques serve as a cornerstone for guaranteeing the quality of implementations. In typical fuzzing workflows, initial seeds are crucial as they form the basis of the process. Literature in traditional program fuzzing has confirmed that effectiveness is largely impacted by redundancy among initial seeds, thereby proposing a series of seed selection methods. JVM fuzzing, compared to traditional ones, presents unique characteristics, including large-scale and intricate code, and programs with both syntactic and semantic features. However, it remains unclear whether the existing initial seed selection methods are suitable for JVM fuzzing and whether utilizing program features can enhance effectiveness. To address this, we devised a total of 10 initial seed selection methods, comprising coverage-based, prefuzz-based, and program-feature-based methods. We then conducted an empirical study on three JVM implementations to extensively evaluate the performance of the initial seed selection methods within two state-of-the-art fuzzing techniques (JavaTailor and VECT). Specifically, we examine performance from three aspects: (i) effectiveness and efficiency using widely studied initial seeds, (ii) effectiveness using the programs in the wild, and (iii) the ability to detect new bugs. Evaluation results first show that the program-feature-based method that utilizes the control flow graph not only has a significantly lower time overhead (i.e., 30s), but also outperforms other methods, achieving 142% to 269% improvement compared to the full set of initial seeds. Second, results reveal that the initial seed selection greatly improves the quality of wild programs and exhibits complementary effectiveness by detecting new behaviors. Third, results demonstrate that given the same testing period, initial seed selection improves the JVM fuzzing techniques by detecting more unknown bugs. Particularly, 16 out of the 25 detected bugs have been confirmed or fixed by developers. This work takes the first look at initial seed selection in JVM fuzzing, confirming its importance in fuzzing effectiveness and efficiency.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | |||
11:00 15mTalk | DPFuzzer: Discovering Safety Critical Vulnerabilities for Drone Path PlannersSecurity Research Track Yue Wang , Chao Yang Xidian University, Xiaodong Zhang , Yuwanqi Deng Xidian University, Jianfeng Ma Xidian University | ||
11:15 15mTalk | IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation Research Track Yuyang Rong University of California, Davis, Zhanghan Yu University of California, Davis, Zhenkai Weng University of California, Davis, Stephen Neuendorffer Advanced Micro Devices, Inc., Hao Chen University of California at Davis | ||
11:30 15mTalk | Ranking Relevant Tests for Order-Dependent Flaky Tests Research Track Shanto Rahman The University of Texas at Austin, Bala Naren Chanumolu George Mason University, Suzzana Rafi George Mason University, August Shi The University of Texas at Austin, Wing Lam George Mason University | ||
11:45 15mTalk | Selecting Initial Seeds for Better JVM Fuzzing Research Track Tianchang Gao Tianjin University, Junjie Chen Tianjin University, Dong Wang Tianjin University, Yile Guo College of Intelligence and Computing, Tianjin University, Yingquan Zhao Tianjin University, Zan Wang Tianjin University | ||
12:00 15mTalk | Toward a Better Understanding of Probabilistic Delta Debugging Research Track Mengxiao Zhang , Zhenyang Xu University of Waterloo, Yongqiang Tian , Xinru Cheng University of Waterloo, Chengnian Sun University of Waterloo | ||
12:15 15mTalk | Tumbling Down the Rabbit Hole: How do Assisting Exploration Strategies Facilitate Grey-box Fuzzing?Award Winner Research Track Mingyuan Wu Southern University of Science and Technology, Jiahong Xiang Southern University of Science and Technology, Kunqiu Chen Southern University of Science and Technology, Peng Di Ant Group & UNSW Sydney, Shin Hwei Tan Concordia University, Heming Cui University of Hong Kong, Yuqun Zhang Southern University of Science and Technology | ||