一句话：提出 DM#，用傅里叶分析以少量数据点量化 DNN 变异体行为并聚类，仅测试代表变异体以加速变异测试。
深度神经网络（DNN）变异分析是评估测试集充分性的重要手段，但由于需要在大规模数据集上测试海量生成的变异体，其代价很高。本文提出 DM# 技术，利用傅里叶分析加速 DNN 变异测试。其核心洞察是：DNN 的输出是实值函数，适合用傅里叶分析刻画，因而仅需少量数据点即可量化变异体行为。DM# 依据这种量化行为对变异体聚类，使行为相似的变异体归入同一组；随后从每组选出一个代表进行测试，并将测试结果（变异体是否被杀死）复用于该组其余变异体，从而免去对它们的实际测试。作者在 14 个参数量从数千到数百万、在不同数据集上训练的 DNN 模型上评估 DM#，并与多种基线方法比较。结果表明，DM# 平均加速变异测试 28.38%，而变异得分平均误差仅 0.72%；与随机变异体选择、边界样本选择和随机样本选择相比，其变异得分误差分别平均降低 11.78、15.16 和 114.36 倍，同时加速效果总体相当。
Deep neural network (DNN) mutation analysis is a promising approach to evaluating test set adequacy. Due to the large number of generated mutants that must be tested on large datasets, mutation analysis is costly. In this paper, we present a technique, named DM#, for accelerating DNN mutation testing using Fourier analysis. The key insight is that DNN outputs are real-valued functions suitable for Fourier analysis that can be leveraged to quantify mutant behavior using only a few data points. DM# uses the quantified mutant behavior to cluster the mutants so that the ones with similar behavior fall into the same group. A representative from each group is then selected for testing, and the result of the test, e.g., whether the mutant is killed or survived, is reused for all other mutants represented by the selected mutant, obviating the need for testing other mutants. 14 DNN models of sizes ranging from thousands to millions of parameters, trained on different datasets, are used to evaluate DM# and compare it to several baseline techniques. Our results provide empirical evidence on the effectiveness of DM# in accelerating mutation testing by 28.38%, on average, at the average cost of only 0.72% error in mutation score. Moreover, on average, DM# incurs 11.78, 15.16, and 114.36 times less mutation score error compared to random mutant selection, boundary sample selection, and random sample selection techniques, respectively, while generally offering comparable speed-up.
DOI 原文 ·
@inproceedings{paperbot9504,
title = {Using Fourier Analysis and Mutant Clustering to Accelerate DNN Mutation Testing},
author = {Ali Ghanbari and Sasan Tavakkol},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00175}
}