迈向对概率性增量调试的更好理解
Toward a Better Understanding of Probabilistic Delta Debugging
AI总结：
本文首次对 ProbDD 进行深入理论与实证分析，揭示其通过跳过低效查询克服 ddmin 瓶颈且随机性影响甚微，并据此提出简化版 CDD，在 76 个基准上以更低复杂度达到同等性能。
摘要：
给定一个元素列表 L 以及 L 所表现出的某个性质，ddmin 是一种著名的测试输入最小化算法，旨在自动消除 L 中不相关的元素。该算法被广泛应用于测试输入最小化和软件去膨胀。最近，ProbDD 作为 ddmin 的一种先进变体被提出，并取得了最先进的性能。ProbDD 采用贝叶斯优化，预测 L 中每个元素为必需元素的概率，并以统计方式决定每次应删除哪些元素以及删除多少元素。尽管其结果令人印象深刻，但 ProbDD 的理论概率模型较为复杂，且驱动其优越性能的具体因素尚未得到研究。在本文中，我们对 ProbDD 进行了首次深入的理论分析，在简化概率模型的同时，阐明了概率和子集大小变化的趋势。作为对该分析的补充，我们进行了实证实验，包括成功率分析、消融研究以及关于权衡与局限性的分析，以更好地理解和揭示这一最先进算法。我们的成功率分析表明，ProbDD 如何通过跳过试图删除子集补集和先前已尝试过的子集的低效查询来解决 ddmin 的瓶颈。消融研究揭示，ProbDD 中的随机性对效率没有显著影响。基于这些发现，我们提出了 CDD，即 ProbDD 的简化版本，降低了理论和实现上的复杂性。此外，CDD 的性能验证了我们的关键发现。在测试输入最小化和软件去膨胀的 76 个基准上的综合评估表明，尽管进行了简化，CDD 仍能达到与 ProbDD 相同的性能。这些见解为测试输入最小化算法的未来研究和应用提供了有价值的指导。
英文摘要：
Given a list L of elements and a property that L exhibits, ddmin is a well-known test input minimization algorithm designed to automatically eliminate irrelevant elements from L. This algorithm is extensively adopted in test input minimization and software debloating. Recently, ProbDD, an advanced variant of ddmin, has been proposed and achieved state-of-the-art performance. Employing Bayesian optimization, ProbDD predicts the likelihood of each element in L being essential, and statistically decides which elements and how many should be removed each time. Despite its impressive results, the theoretical probabilistic model of ProbDD is complex, and the specific factors driving its superior performance have not been investigated. In this paper, we conduct the first in-depth theoretical analysis of ProbDD, clarifying trends in probability and subset size changes while simplifying the probability model. Complementing this analysis, we perform empirical experiments, including success rate analysis, ablation studies, and analysis on trade-offs and limitations, to better understand and demystify this state-of-the-art algorithm. Our success rate analysis shows how ProbDD addresses bottlenecks of ddmin by skipping inefficient queries that attempt to delete complements of subsets and previously tried subsets. The ablation study reveals that randomness in ProbDD has no significant impact on efficiency. Based on these findings, we propose CDD, a simplified version of ProbDD, reducing complexity in both theory and implementation. Besides, the performance of CDD validates our key findings. Comprehensive evaluations across 76 benchmarks in test input minimization and software debloating show that CDD can achieve the same performance as ProbDD despite its simplification. These insights provide valuable guidance for future research and applications of test input minimization algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
