计算机视觉智能测试建模与生成：以智能OCR为例
Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
- San Jose State University（圣何塞州立大学）
- ALPSTouchStone, Inc.（ALPSTouchStone公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对AI软件质量评估难题，提出含3D分类模型、测试覆盖准则与四项评估指标的AI软件功能测试框架，并通过移动端OCR案例验证其有效性。
AI中文摘要：
基于AI的系统具有独特特性，同时也给质量评估带来了挑战。因此，确保并验证AI软件质量至关重要。本文提出了一种有效的AI软件功能测试模型以应对这一挑战。具体而言，我们首先对已有研究进行了全面的文献综述，涵盖AI软件测试流程的关键方面。随后，我们引入了一种3D分类模型，用于系统评估基于图像的文本提取AI功能，同时还提出了测试覆盖准则与复杂度指标。为评估所提出的AI软件质量测试的性能，我们设计了四项评估指标以覆盖不同维度。最后，基于所提出的框架和定义的指标，我们开展了一项移动端Optical Character Recognition（OCR，光学字符识别）案例研究，验证了该框架在评估AI功能质量方面的有效性与能力。
英文摘要：
AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we present an effective AI software functional testing model to address this challenge. Specifically, we first present a comprehensive literature review of previous work, covering key facets of AI software testing processes. We then introduce a 3D classification model to systematically evaluate the image-based text extraction AI function, as well as test coverage criteria and complexity. To evaluate the performance of our proposed AI software quality test, we propose four evaluation metrics to cover different aspects. Finally, based on the proposed framework and defined metrics, a mobile Optical Character Recognition (OCR) case study is presented to demonstrate the framework's effectiveness and capability in assessing AI function quality.
