预算花到哪里去了？基于学习的变异体选择中的结构集中性

中文摘要 AI 辅助

基于学习的变异体选择通过排序并选择看似最有前景的变异体来降低变异测试的成本。先前的工作已显示该方法在故障揭示和包含变异体选择方面具有巨大潜力。然而，这些方法的风险与收益——尤其是所选变异体在代码位置上的分布以及该分布如何影响行为多样性和故障揭示——仍不清楚。我们研究了这一分布及其影响，并引入了LinePool，一个简单的、与模型无关的分层步骤，它在保留底层排序信号的同时将选择分配到各源代码行。在两个数据集和预算（变异体的2%和5%）下，我们发现基于学习的选择将预算集中在少数代码位置，并可能因此系统性地引入“盲区”，即未测试的代码区域，从而使故障得以逃脱检测。在较大的程序上，所选变异体在杀死行为上也表现出显著的重叠。LinePool显著提高了空间覆盖率并减少了杀死集重叠。它还在紧张的预算下改善了故障揭示，并使选择对不完善或偏移的排序信号更加稳健。与聚类和已确立的多样化方法的比较表明，没有一种方法在所有数据集上始终优于LinePool，这支持将其用作一个简单的多样化步骤。总体而言，我们的结果表明，在设计和评估变异体选择方法时，应将行为多样性与有效性一并考虑，而利用程序的结构元素是获得这种多样性同时保持有效性的一种简单途径。

英文摘要

Learning-based mutant selection is used to reduce the cost of mutation testing by ranking and selecting the mutants that seem to be the most promising. Prior work has shown great potential for this approach in fault revelation and subsuming-mutant selection. However, the risks and benefits of these approaches -- especially the distribution of selected mutants across code locations and how that distribution affects behavioral diversity and fault revelation -- remain unclear. We investigate this distribution and its effects, and introduce LinePool, a simple, model-agnostic stratification step that distributes selections across source lines while retaining the underlying ranking signal. Across two datasets and budgets (2% and 5% of the mutants), we find that learning-based selection concentrates the budget on a few code locations, and may thereby systematically introduce 'blind spots', untested code areas, allowing faults to escape detection. On larger programs, the selected mutants also exhibit substantial overlap in kill behavior. LinePool substantially increases spatial coverage and reduces kill-set overlap. It also improves fault revelation at tight budgets and makes selection more robust to imperfect or shifted ranking signals. Comparisons with clustering and established diversification methods show that none consistently outperforms LinePool across datasets, supporting its use as a simple diversification step. In general, our results suggest that behavioral diversity should be considered alongside effectiveness when designing and evaluating mutant-selection methods, and that exploiting structural elements of the program is one simple way to obtain such diversity while maintaining effectiveness.

发表机构

• University of Luxembourg（卢森堡大学）
• Kyungpook National University（庆北国立大学）

机构由 AI 辅助整理，请以论文原文为准。