Online testing of RESTful APIs: promises and challenges

Alberto Martin-Lopez, Sergio Segura, Antonio Ruiz-Cortés
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引用次数: 9

Abstract

Online testing of web APIs—testing APIs in production—is gaining traction in industry. Platforms such as RapidAPI and Sauce Labs provide online testing and monitoring services of web APIs 24/7, typically by re-executing manually designed test cases on the target APIs on a regular basis. In parallel, research on the automated generation of test cases for RESTful APIs has seen significant advances in recent years. However, despite their promising results in the lab, it is unclear whether research tools would scale to industrial-size settings and, more importantly, how they would perform in an online testing setup, increasingly common in practice. In this paper, we report the results of an empirical study on the use of automated test case generation methods for online testing of RESTful APIs. Specifically, we used the RESTest framework to automatically generate and execute test cases in 13 industrial APIs for 15 days non-stop, resulting in over one million test cases. To scale at this level, we had to transition from a monolithic tool approach to a multi-bot architecture with over 200 bots working cooperatively in tasks like test generation and reporting. As a result, we uncovered about 390K failures, which we conservatively triaged into 254 bugs, 65 of which have been acknowledged or fixed by developers to date. Among others, we identified confirmed faults in the APIs of Amadeus, Foursquare, Yelp, and YouTube, accessed by millions of applications worldwide. More importantly, our reports have guided developers on improving their APIs, including bug fixes and documentation updates in the APIs of Amadeus and YouTube. Our results show the potential of online testing of RESTful APIs as the next must-have feature in industry, but also some of the key challenges to overcome for its full adoption in practice.
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RESTful api的在线测试:承诺与挑战
web api的在线测试——在生产环境中测试api——在工业中越来越受欢迎。RapidAPI和Sauce Labs等平台提供全天候的web api在线测试和监控服务,通常是定期在目标api上重新执行手动设计的测试用例。与此同时,对RESTful api测试用例自动生成的研究近年来取得了重大进展。然而,尽管这些研究工具在实验室中取得了可喜的成果,但目前尚不清楚它们是否可以扩展到工业规模,更重要的是,它们在实践中日益普遍的在线测试设置中如何表现。在本文中,我们报告了一项关于使用自动化测试用例生成方法在线测试RESTful api的实证研究结果。具体来说,我们使用rest框架在13个工业api中自动生成和执行测试用例,连续15天不间断,产生了超过一百万个测试用例。为了在这个级别上进行扩展,我们必须从单一的工具方法过渡到多机器人架构,其中有超过200个机器人在测试生成和报告等任务中协同工作。结果,我们发现了大约390K个故障,我们保守地将其分类为254个错误,其中65个已经被开发人员承认或修复。其中,我们在Amadeus、Foursquare、Yelp和YouTube的api中发现了已确认的错误,全球有数百万应用程序访问这些api。更重要的是,我们的报告指导开发人员改进他们的api,包括修复Amadeus和YouTube api中的错误和文档更新。我们的研究结果显示了RESTful api在线测试作为工业界下一个必备特性的潜力,但也显示了在实践中全面采用RESTful api需要克服的一些关键挑战。
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