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

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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.