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

通过故障分析提高云原生应用程序的质量和开发人员生产力

• 批准号: 558283-2020
• 负责人: Liscano, Ramiro
• 金额: $ 1.7万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721684
• 关键词: Quality; Developer; Productivity; Enhancements; Cloud

This project investigates machine learning and data mining techniques to identify and locate faults of cloud-nativemicro-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 assuch do not surface as software failures. This makes it difficult to identify and localize the fault as it could becaused from different components in the system, system and compiler settings, as well as inefficientinteractions among the components in the system.A machine learning approach will be investigated for the detection, identification, and localization of thefaults. Its effectiveness will be evaluated based on its ability to correlate runtime trace faults to applicationcode structure, configuration parameters, and/or component interaction behaviours.As a recommendation system, we envision that once the fault is identified we would search for therecommended 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 potentialissues and suggested fixes.The industrial partner supports an open development platform for Java-based cloud micro-services and are inneed of such tools. Currently, no such tools are available that can examine runtime faults and recommendchanges to the developer for cloud-native micro-service applications.

该项目研究机器学习和数据挖掘技术，以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来识别和定位云原生微服务应用程序的故障。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其自身暴露为应用程序中的性能降级故障，并且因此不会以软件故障的形式出现。这使得识别和定位故障变得困难，因为它可能是由于系统中的不同组件，系统和编译器设置，以及系统中组件之间的低效交互造成的。机器学习方法将被研究用于检测，识别和定位故障。其有效性将根据其将运行时跟踪故障与应用程序代码结构、配置参数和/或组件交互行为相关联的能力进行评估。作为推荐系统，我们设想，一旦确定了故障，我们将搜索针对故障类型的推荐软件开发指南，并将此信息呈现给开发人员。该推荐可以以两种模式应用：作为开发人员编码工作流的一部分，或作为生成潜在问题报告和建议修复的独立工具，在集成开发环境（IDE）中进行交互。工业合作伙伴支持基于Java的云微服务的开放开发平台，并且需要此类工具。目前，还没有这样的工具可以检查运行时故障并将更改提交给云原生微服务应用程序的开发人员。