Toward a Better Understanding of Probabilistic Delta Debugging
This program is tentative and subject to change.
Given a list L of elements and a property ψ that L exhibits, ddmin is a classic test input minimization algorithm that aims to automatically remove ψ-irrelevant elements from L. This algorithm has been widely adopted in domains such as test input minimization and software debloating. Recently, ProbDD, a variant of ddmin, has been proposed and achieved stateof- the-art performance. By employing Bayesian optimization, ProbDD estimates the probability of each element in L being relevant to ψ, and statistically decides which and how many elements should be deleted together each time. However, the theoretical probabilistic model of ProbDD is rather intricate, and the underlying details for the superior performance of ProbDD have not been adequately explored.
In this paper, we conduct the first in-depth theoretical analysis of ProbDD, clarifying the trends in probability and subset size changes and simplifying the probability model. We complement this analysis with empirical experiments, including success rate analysis, ablation studies, and examinations of trade-offs and limitations, to further comprehend and demystify this state-of- the-art algorithm. Our success rate analysis reveals how ProbDD effectively addresses bottlenecks that slow down ddmin by skipping inefficient queries that attempt to delete complements of subsets and previously tried subsets. The ablation study illustrates that randomness in ProbDD has no significant impact on efficiency. These findings provide valuable insights for future research and applications of test input minimization algorithms.
Based on the findings above, we propose CDD, a simplified version of ProbDD, reducing the complexity in both theory and implementation. CDD assists in 1 validating the correctness of our key findings, e.g., that probabilities in ProbDD essentially serve as monotonically increasing counters for each element, and 2 identifying the main factors that truly contribute to ProbDD’s superior performance. Our comprehensive evaluations across 76 benchmarks in test input minimization and software debloating demonstrate that CDD can achieve the same performance as ProbDD, despite being much simplified.
This program is tentative and subject to change.
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 Planners 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 Hong Kong University of Science and Technology, 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, Shin Hwei Tan Concordia University, Heming Cui University of Hong Kong, Yuqun Zhang Southern University of Science and Technology | ||