构建AI辅助测试分类体系，梳理智能测试研究脉络与关键问题
Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
- 提出ai4st分类框架，系统划分从人工到全自动化测试的AI应用
- 梳理近年研究进展，识别出测试用例生成、缺陷预测等核心方向
- 适合关注AI+软件测试的工程师与研究人员参考
在工业界，软件测试是验证和评估软件系统功能、性能、安全性和可用性等的关键手段。过去十年间，测试自动化受到业界越来越多关注，这建立在数十年测试自动化与基于模型测试的研究基础之上。然而，测试自动化的设计、开发、维护与演化仍需大量投入。与此同时，人工智能在众多工程领域的突破为软件测试（包括手工和自动化）带来了新视角。本文综述了近期关于人工智能增强软件测试自动化研究，涵盖从无自动化到完全自动化的演进过程，并探讨了由AI催生的新测试形式。基于此，本文提出了新的分类体系ai4st，用于对近期研究进行归类，并识别出待解决的研究问题。
原文摘要 · Abstract (English)
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening up new perspectives for software testing, for both manual and automated testing. This paper reviews recent research on AI augmentation in software test automation, from no automation to full automation. It also discusses new forms of testing made possible by AI. Based on this, the newly developed taxonomy, ai4st, is presented and used to classify recent research and identify open research questions.
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