Toward a Better Understanding of Probabilistic Delta Debugging
Mengxiao Zhang, Zhenyang Xu, Yongqiang Tian, Xinru Cheng, Chengnian Sun

TL;DR
This paper provides a theoretical and empirical analysis of ProbDD, a state-of-the-art probabilistic delta debugging algorithm, introduces a simplified version called CDD, and demonstrates its comparable performance across various benchmarks.
Contribution
It offers the first in-depth theoretical analysis of ProbDD, proposes CDD as a simplified alternative, and validates its effectiveness through extensive experiments.
Findings
ProbDD skips inefficient queries, improving performance.
Randomness in ProbDD has minimal impact on efficiency.
CDD achieves similar results to ProbDD despite simplification.
Abstract
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…
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Taxonomy
TopicsReservoir Engineering and Simulation Methods · Hydraulic Fracturing and Reservoir Analysis
