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: $ 1.62万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706726
• 关键词: Quality; Developer; Productivity; Enhancements; Cloud

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.

本项目研究了机器学习和数据挖掘技术来识别和定位云原生故障