一句话：报告将基于搜索的测试应用于自动驾驶控制应用的经验，并总结工业落地的经验教训。
本文是一篇经验论文，关注自动驾驶（AD）应用验证中仿真环境下的自动化测试生成与评估。基于搜索的测试（SBT）利用优化技术，从抽象测试描述出发高效生成有意义的 concrete tests。作者将 SBT 成功应用于多个特征各异的 AD 控制应用用例，并据此总结出若干对工业界采用 SBT 方法与工具至关重要的经验教训。核心结论是：SBT 能够发现相关错误，并为开发人员提供有价值的反馈，但其推广仍依赖对编写规约的工具支持。
Automated test generation and evaluation in simulation environments is a key technology for verification of automated driving (AD) applications. Search-based testing (SBT) is an approach for automated test generation that leverages optimization to efficiently generate interesting concrete tests from abstract test descriptions. In this experience paper, we report on our observations after successfully applying SBT to AD control applications in several use cases with different characteristics. Based on our experiences, we derive a number of lessons learned that we consider important for the adoption of SBT methods and tools in industrial settings. The key lesson is that SBT finds relevant errors and provides valuable feedback to the developers, but requires tool support for writing specifications.
DOI 原文 ·
@inproceedings{paperbot5294,
title = {Experience Paper: Search-Based Testing in Automated Driving Control Applications},
author = {Christoph Gladisch and Thomas Heinz and Christian Heinzemann and Jens Oehlerking and Anne von Vietinghoff and Tim Pfitzer},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00013}
}