Quality and Developer Productivity Enhancements for Cloud-Native Applications via Fault Analysis & Localization with Machine Learning

该项目研究机器学习和数据挖掘技术，以识别和定位云原生的故障 微服务应用程序基于由格式不佳的代码和系统配置设置引起的运行时行为。它还研究了一个推荐系统的开发，以帮助开发人员减轻这些运行时的错误，一旦他们已经确定和定位。 这些运行时故障通常将它们自己暴露为应用程序中的性能降级故障， 这不会以软件故障的形式出现。这使得很难识别和定位故障，因为它可能是 造成系统中的组件、系统和编译器设置不同，以及效率低下 系统中各组件之间的相互作用。 将研究机器学习方法用于检测、识别和定位 断层其有效性将根据其将运行时跟踪故障与应用程序相关联的能力进行评估 代码结构、配置参数和/或组件交互行为。 作为一个推荐系统，我们设想，一旦确定了故障，我们将搜索 故障类型的推荐软件开发指南，并将此信息提供给开发人员。 该建议可以以两种模式应用：在集成开发环境中交互式地应用 (IDE)作为开发人员编码工作流程的一部分，或作为一个独立的工具， 问题和建议的修复。 该行业合作伙伴支持基于Java的云微服务的开放式开发平台，并在 需要这些工具。目前，还没有这样的工具可以检查运行时故障并建议 对云原生微服务应用程序的开发人员进行更改。

This project investigates machine learning and data mining techniques to identify and locate faults of cloud-native micro-service applications based on runtime behaviours caused by poorly formed code and system configuration settings. It also investigates the development of a recommendation system to help developers mitigate these run-time faults once they have been identified and located. These run time faults typically expose themselves as performance degradation faults in the application and as such do not surface as software failures. This makes it difficult to identify and localize the fault as it could be caused from different components in the system, system and compiler settings, as well as inefficient interactions among the components in the system. A machine learning approach will be investigated for the detection, identification, and localization of the faults. Its effectiveness will be evaluated based on its ability to correlate runtime trace faults to application code structure, configuration parameters, and/or component interaction behaviours. As a recommendation system, we envision that once the fault is identified we would search for the recommended software development guides for the fault types and present this information to the developer. The recommendation could be applied in two modes: interactively in an Integrated Development Environment (IDE) as part of the developer's coding workflow or as a standalone tool that produces a report of potential issues and suggested fixes. The industrial partner supports an open development platform for Java-based cloud micro-services and are in need of such tools. Currently, no such tools are available that can examine runtime faults and recommend changes to the developer for cloud-native micro-service applications.