一句话：提出 GBV-PD，首次将行为向量空间用于演化感知的回归测试优先级排序，无需运行演化后模型即可预测测试用例损失的升降。
ML 赋能系统中的机器学习组件会通过重训练、微调和优化不断演化，导致原有测试结果失效；且单次演化可能改善部分测试用例而恶化另一些，使回归测试优先级排序本质上具有方向性。本文提出 GBV-PD，首次将行为向量空间操作化用于演化感知的回归测试优先级排序。该方法将每个测试用例表示为原始模型下损失梯度的低维投影，即基于梯度的行为向量（GBV），再把演化模型的参数更新投影到同一 PCA 基上，利用两者的对齐关系估计各测试用例损失可能上升还是下降，且排序过程中无需在测试用例上运行演化后模型；可复用的 GBV 缓存还改善了重复更新的时间与存储开销。在分类与回归任务上的实证研究表明，GBV-PD 持续优于非方向性基线，并与全梯度参考方法保持竞争力。
The machine learning(ML) component of an ML-enabled system evolves through retraining, fine-tuning, and optimization, so previously valid test results may no longer hold. A single evolution step can worsen performance on some test cases while improving others, making regression test prioritization inherently directional. We present Gradient-based Behavior Vector-Parameter Delta(GBV-PD), the first approach to operationalize the behavior vector space for evolution-aware regression test prioritization. GBV-PD represents each test case as a gradient-based vector(GBV), a low-dimensional projection of its loss gradient under the original model. It then projects the observed parameter update of the evolved model onto the same PCA basis and uses the resulting alignment to estimate whether each test case's loss is likely to increase or decrease, without running the evolved model on test cases during prioritization. In an empirical study across classification and regression tasks, GBV-PD consistently outperformed non-directional baselines and remained competitive with a full-gradient reference, while offering better time and storage profiles for repeated updates via reusable GBV caching. These results show that behavior-space ideas can be operationalized into a practical and efficient mechanism for repeated-update regression-test prioritization of evolving ML components within ML-enabled systems.
@inproceedings{abs-2606-28037,
title = {Evolution-Aware Regression Test Prioritization of ML-Enabled Systems using Gradient-Based Behavior Vectors},
author = {Eunho Cho and Donghwan Shin and In-Young Ko},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834368}
}