Intelligent Incident Management for Software-Intensive Systems. This project aims to develop intelligent incident management methods for software-intensive systems. Incidents are unplanned system interruptions or outages that could affect the normal operations of an organization and cause huge economic loss. This project expects to develop innovative, Artificial Intelligence (AI) based methods for automated incident management, including incident detection, incident identification, and incident ....Intelligent Incident Management for Software-Intensive Systems. This project aims to develop intelligent incident management methods for software-intensive systems. Incidents are unplanned system interruptions or outages that could affect the normal operations of an organization and cause huge economic loss. This project expects to develop innovative, Artificial Intelligence (AI) based methods for automated incident management, including incident detection, incident identification, and incident triage. Expected outcomes of the project include a set of novel methods and tools that can facilitate incident diagnosis and resolution. This project will provide significant benefits, such as improving the availability of software-intensive systems and reducing the economic loss caused by the incidents. Read moreRead less
Data-driven Approach to Resilient Online Service Systems. This project aims to develop a data-driven approach to improving the resilience of online service systems. Many software systems are now provided as online services via the Internet on a 24/7 basis. Although a lot of effort has been devoted to service quality assurance, in reality, online service systems still encounter many incidents and fail to satisfy user requests. This project expects to develop innovative data-driven methods for eff ....Data-driven Approach to Resilient Online Service Systems. This project aims to develop a data-driven approach to improving the resilience of online service systems. Many software systems are now provided as online services via the Internet on a 24/7 basis. Although a lot of effort has been devoted to service quality assurance, in reality, online service systems still encounter many incidents and fail to satisfy user requests. This project expects to develop innovative data-driven methods for effective fault identification, fault localization, and failure prediction. Expected outcomes of this project include novel techniques and tools for maintaining online service systems. This project will provide significant benefits, such as improving the resilience and reliability of our cyber infrastructure.Read moreRead less