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.

该项目研究了机器学习和数据挖掘技术，以识别和定位基于由不良的代码和系统配置设置引起的运行时行为的云 - 纳蒂维科服务应用程序的故障。它还研究了推荐系统的开发，以帮助开发人员一旦确定并找到了这些运行时的故障。这些运行的时间故障通常会随着应用程序的性能降解故障而表现出来，而Assuch不会随着软件故障而浮出水面。这使得很难识别和本地化故障，因为它可能是从系统，系统和编译器设置中的不同组件中进行的，以及系统中组件之间的效率低下。将根据其将运行时跟踪故障与应用程序尺寸结构，配置参数和/或组件互动行为相关联的能力来评估其有效性。作为建议系统，我们设想一旦确定了故障，我们将搜索其他软件开发指南，以便在开发人员中搜索此信息，并将其呈现为开发人员。工作流程或作为产生潜在信息报告和建议修复的独立工具。工业合作伙伴支持基于Java的云微服务的开放开发平台，并且是此类工具。当前，尚无此类工具可以检查运行时故障并向开发人员推荐用于云的微服务应用程序。