一句话：提出用机器学习元启发式近似结合现有方法，为满足 MC/DC 的安全关键软件生成测试数据，缓解路径爆炸问题。
本文关注安全关键系统开发中基于 MC/DC 准则的单元测试。当前满足 MC/DC 的测试数据设计仍依赖测试工程师对分支条件的详细人工分析；已有方法虽有工业应用，但存在尚未完全解决的路径爆炸等问题。作者提出将机器学习方法作为元启发式近似，用来建模传统方法难以测试、且会发生路径爆炸的程序行为，并与现有测试数据生成方法结合，以克服其局限。基于与工业伙伴的持续合作，作者认为这种结合有望形成适用于安全关键项目测试的方案。摘要未报告具体实验或结果，评估尚待后续工作验证。
Unit testing focused on Modified Condition/Decision Coverage (MC/DC) criterion is essential in development safety-critical systems. However, design of test data that meets the MC/DC criterion currently needs detailed manual analysis of branching conditions in units under test by test engineers. Multiple state-of-art approaches exist with proven usage even in industrial projects. However, these approaches have multiple shortcomings, one of them being the Path explosion problem which has not been fully solved yet. Machine learning methods as meta-heuristic approximations can model behaviour of programs that are hard to test using traditional approaches, where the Path explosion problem does occur and thus could solve the limitations of the current state-of-art approaches. I believe, motivated by an ongoing collaboration with an industrial partner, that the machine learning methods could be combined with existing approaches to produce an approach suitable for testing of safety-critical projects.
DOI 原文 ·
@inproceedings{paperbot5743,
title = {Machine learning based test data generation for safety-critical software},
author = {Ján Čegiň},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3418538}
}