Today, Serverless computing gathers pace and attention in the cloud computing area. The abstraction of operational tasks combined with the auto-scaling property are convincing reasons to adapt this new cloud paradigm. Building applications in a Serverless style via cloud functions is challenging due to the fine-grained architecture and the tighter coupling to back end services. Increased complexity, loss of control over software layers and the large number of participating functions and back end services complicate the task of finding the cause of a faulty execution. A tedious but widespread strategy is the manual analysis of log data. In this paper, we present a semi-automated troubleshooting process to improve fault detection and resolution for Serverless functions. Log data is the vehicle to enable a posteriori analysis. The process steps of our concept enhance the log quality, detect failed executions automatically, and generate test skeletons based on the information provided in the log data. Ultimately, this leads to an increased test coverage, a better regression testing and more robust functions. Developers can trigger this process asynchronously and work with their accustomed tools. We also present a prototype SeMoDe to validate our approach for Serverless functions implemented in Java and deployed to AWS Lambda.

中文翻译：

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无服务器功能故障排除：组合的监视和调试方法

如今，无服务器计算在云计算领域引起了人们的关注和关注。结合自动缩放属性的操作任务的抽象是令人信服的理由，以适应这种新的云范式。通过细粒度的架构以及与后端服务的紧密耦合，通过云功能以无服务器风格构建应用程序具有挑战性。增加的复杂性，对软件层的控制的丧失以及大量参与功能和后端服务使查找错误执行原因的任务变得复杂。繁琐而广泛的策略是手动分析日志数据。在本文中，我们提出了一种半自动的故障排除过程，以改进无服务器功能的故障检测和解决方案。日志数据是进行后验分析的工具。我们概念的处理步骤提高了日志质量，自动检测失败的执行，并根据日志数据中提供的信息生成测试框架。最终，这会导致测试覆盖面增加，回归测试更好并且功能更强大。开发人员可以异步触发此过程，并使用他们惯用的工具。我们还展示了一个原型SeMoDe验证了我们针对以Java实现并部署到AWS Lambda的无服务器功能的方法。