BISCEPTER：面向大规模系统软件的概率驱动二分查找

中文摘要 AI 辅助

识别引入缺陷的提交（BIC）是回归调试中的基本步骤，也是新兴的BIC感知故障定位流程的关键输入。在实践中，BIC通常通过二分查找获得。标准二分查找选择剩余好-坏区间中位数的提交，从而平衡提交数量。该策略在假设每个提交成为BIC的概率相等时是最优的。本文表明，这一假设与真实世界的BIC历史不符。我们构建了一个包含来自GCC、Linux内核和MariaDB的8,172个缺陷报告的数据集。我们发现存在强烈的时间偏差：在所研究的系统中，50%的BIC位于报告时提交历史中最近的0.69%内。受此观察启发，我们引入了BISCEPTER，一种概率驱动的二分查找方法，利用历史BIC延迟作为轻量级先验。BISCEPTER不是选择分割剩余提交数量的枢轴，而是选择加权中位数枢轴，以分割估计的BIC概率质量，同时保持与标准二分查找相同的好-坏预言和接口。我们在三个大规模系统上评估了BISCEPTER。评估结果表明，与标准中位数二分查找相比，BISCEPTER平均减少了25.75%（最高达55.55%）的二分迭代次数，并在91.26%的测试用例中优于基线。鲁棒性实验进一步表明，在历史数据存在噪声的情况下，该优势仍然稳定。我们期望我们的研究能够在实践中有效节省软件调试的工作量，并且更广泛地，通过提供关于BIC分布的见解，惠及未来的软件工程研究。

英文摘要

Identifying the bug-inducing commit (BIC) is a fundamental step in regression debugging and a key input to emerging BIC-aware fault-localization pipelines. In practice, BICs are commonly obtained with bisection. Standard bisection selects the median commit of the remaining good-bad interval, thereby balancing commit count. This strategy is optimal under the assumption that each commit is equally likely to be the BIC. This paper shows that this assumption does not match real-world BIC histories. We construct a dataset of 8,172 bug reports from GCC, the Linux kernel, and MariaDB. We find a strong temporal skew: across the studied systems, 50% of BICs lie within the most recent 0.69% of the report-time commit history. Motivated by this observation, we introduce BISCEPTER, a probability-driven bisection approach that uses historical BIC latency as a lightweight prior. Instead of selecting pivots that split the number of remaining commits, BISCEPTER selects weighted-median pivots that split estimated BIC probability mass, while preserving the same good-bad oracle and interface as standard bisection. We evaluate BISCEPTER on three large-scale systems. The evaluation results show that BISCEPTER reduces bisection iterations by 25.75% on average (up to 55.55%) compared with standard median bisection, while improving over the baseline in 91.26% of test cases. Robustness experiments further demonstrate that the benefit remains stable under noisy historical data. We expect that our research can effectively save effort in debugging software in practice and, more broadly, benefit future software engineering research by bringing insights about BIC distribution.

发表机构

• The University of Hong Kong（香港大学）
• ETH Zürich（苏黎世联邦理工学院）

机构由 AI 辅助整理，请以论文原文为准。