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

该项目研究了机器学习和数据挖掘技术，以识别和定位云本地的故障 基于错误代码和系统配置设置导致的运行时行为的微服务应用程序。它还调查了推荐系统的开发，以帮助开发人员在识别和定位这些运行时错误后减轻这些错误。 这些运行时错误通常将自身暴露为应用程序中的性能下降错误和 这种情况不会以软件故障的形式浮出水面。这使得识别和定位故障变得非常困难 由于系统中不同的组件、系统和编译器设置，以及效率低下 系统中组件之间的交互。 将研究一种机器学习方法来检测、识别和定位 有缺陷。它的有效性将根据其将运行时跟踪错误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，我们设想一旦发现故障，我们将搜索 针对故障类型推荐的软件开发指南，并将此信息提供给开发人员。 该建议可通过两种模式适用：在综合发展环境中以交互方式实施 (IDE)作为开发人员编码工作流程的一部分，或作为生成潜力报告的独立工具 问题和建议的修复。 该行业合作伙伴支持基于Java的云微服务的开放开发平台，并在 需要这样的工具。目前，还没有这样的工具可以检查运行时故障并建议 对云本地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.