ADEPT：深度学习测试充分性的统一框架
ADEPT: A Unified Framework for Deep Learning Test Adequacy
- Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出 ADEPT 框架，整合多种深度学习测试充分性指标，提供统一工作流与配置管理，解决指标难复现、难比较问题，方便研究人员和从业者使用。
AI中文摘要：
过去十年间，人们针对深度学习提出了多种测试充分性指标，这些指标从不同角度表征测试数据集的充分性，例如神经元激活行为、潜在特征覆盖范围、决策边界探索等。然而，这些指标通常以独立的研究原型形式发布，具有截然不同的安装、预处理要求、执行工作流和配置机制，这些复杂因素使得它们在研究工作和实际部署中都极难复现、比较和采用。本文介绍了 ADEPT 的工程细节，该框架整合了具有代表性的充分性技术，包括基于神经元覆盖的指标、惊喜充分性、输入分布覆盖、边界覆盖以及源级和模型级变异分数，并将其置于一致的执行工作流下。ADEPT 提供了基于模板的指标接口，具有定义明确的扩展点，用于集成新的充分性指标；此外，它还提供基于 YAML 的配置管理、预处理缓存复用和结构化结果报告，使其易于在任何研究和开发工作流中使用。ADEPT 面向希望复现和应用充分性指标，而无需花费数天或数周时间实现缺失工具或配置不同研究原型的研究人员和从业者，演示视频可在该 https URL 获取。
英文摘要：
Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation behavior, latent feature coverage, decision-boundary exploration, etc. However, these metrics are typically released as independent research prototypes with substantially different installation and preprocessing requirements, execution workflows, and configuration mechanisms. These complications make them quite difficult to reproduce, compare, and adopt in research work and practical deployment alike. In this paper, we present the engineering details of ADEPT, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow. ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics. Furthermore, it provides YAML-based configuration management, preprocessing-cache reuse, and structured result reporting, making it easy to use in any research and development workflows. ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling or configuring disparate research prototypes. A demo video is available at https://aub.ie/ADEPT_video.
