Grant: Collaborative Research and Development Grants

Mainframe platforms play a central role in supporting mission-critical services that are used by millions of users, such as payroll services, payment services and customer ordering processing services. During the software execution, applications on mainframe platforms are continuously monitored to check the performance and reliability of the applications by tracing a set of key performance metrics and recording the results of the metrics into execution logs. In the current state of practices, the analysis of the execution logs is limited to understanding the already happened events (e.g., crashes and performance bottlenecks). In this proposed project, we plan to apply machine learning techniques to predict future crashes and performance issues by analyzing the historical execution logs. Moreover, we will link the run-time problems with the development to predict the possible run-time failures (e.g., performance bottlenecks) during the development phrase. We will design and develop techniques to improve software performance (e.g., CPU usage, memory usage) in the development stage. Our techniques will give developers early warnings for performance degradation and fix the possible defects before product delivery. The results of the project aim to provide actionable information for the developers to make informed decisions, improve their productivity and enhance the quality and performance of mainframe software systems. The project will train 8 HQPs (i.e., 2PhD, 3MSc, 2BSc and 1RA) in an important and critical topic for applying machine learning and artificial intelligence to the software development operations.