Grant: Alliance Grants

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.