Test Optimization in DNN Testing: A Survey

IF 6.6 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

ACM Transactions on Software Engineering and Methodology Pub Date : 2024-01-27 DOI:10.1145/3643678

Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

引用次数: 0

Abstract

This paper presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.

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DNN 测试中的测试优化：调查

本文对深度神经网络（DNN）测试中的测试优化进行了全面研究。这里的测试优化指的是以较低的数据标注工作量进行测试。我们分析了 90 篇论文，其中 43 篇来自软件工程（SE）领域，32 篇来自机器学习（ML）领域，15 篇来自其他领域。我们的研究：(i) 统一了与低标注成本测试相关的问题和术语，(ii) 比较了 SE 和 ML 社区的不同焦点，(iii) 揭示了现有文献中的误区。此外，我们还强调了该领域的研究机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Software Engineering and Methodology 工程技术-计算机：软件工程

CiteScore

6.30

自引率

4.50%

发文量

164

审稿时长

>12 weeks

期刊介绍： Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.