一句话：提出以人为中心的混合方法评估框架，衡量 REST API 模糊测试工具的可用性与认知负荷，弥合学术与工业落地差距。
随着云原生微服务架构日益复杂，自动化测试对系统的可靠性与安全性至关重要。学术界的 REST API 模糊测试工具（如 EvoMaster、Schemathesis）虽有较强的故障检测能力，但因缺乏针对可用性、可学习性与工程集成的以人为中心评估，在工业界采用率有限。该博士项目提出一套混合方法、以人为中心的评估方案，分三阶段推进：第一阶段系统梳理自动化测试领域的现有实证方法；第二阶段在学生中开展受控实验室研究，评估工具可用性与认知负荷；第三阶段将评估延伸至真实工业场景。项目通过对技术性能与认知、行为、感知数据进行三角互证，期望产出可复用的实证方法与设计建议，使学术进展更契合真实开发需求。
As software systems grow in complexity—especially in cloud-based microservice architectures, automated testing has become crucial for reliability and security. While academic REST API fuzzing tools (e.g., EvoMaster, Schemathesis) show strong fault detection, their adoption in industry is limited due to insufficient human-centered evaluation focusing on usability, learnability, and integration. This PhD project will address the gap through a mixed-methods, human-centered evaluation approach. Phase 1 will review current empirical methods in automated testing. Phase 2 will run controlled lab studies with students to evaluate tool usability and cognitive load. Phase 3 will bring the evaluation to real-world industrial settings. By triangulating technical performance with cognitive, behavioral, and perceptual data, this project aims to foster more usable and effective testing tools. It will contribute empirical methods and design recommendations to align academic advancements with real-world development needs.
DOI 原文 ·
@inproceedings{paperbot9312,
title = {Human-Centered Evaluation of REST API Fuzzing Tools: Bridging Academia and Industry},
author = {Fanny Febriani Susilo},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00395}
}