D2K+: Deep Learning of System Crash and Failure Reports for DevOps

The objective of this project is to develop techniques and tools that leverage artificial intelligence to automate the process of handling system crashes at Ericsson, one of the largest telecom and software companies in the world, and where the handling of crash reports (CRs) and continuous monitoring of key infrastructures tend to be particularly complex due to the large client base the company serves. In this project, we will explore the use of deep learning algorithms to classify CRs based on a variety of features including crash traces, CR descriptions, and a combination of both. Crashes in the same group can be processed in a similar way, reducing the time to process each crash separately. In addition, we will conduct qualitative studies in order to understand the relationship between CRs and faults, providing Ericsson teams with the ability to prioritize problems, collect statistics, and gain actionable insights into their deployed systems. Furthermore, this project will be essential in conducting root cause analysis of faults and crashes, developing organizational guidelines for CR reporting, and setting the ground work for powerful operational intelligence capabilities.

Faculty Supervisor:

Abdelwahab Hamou-Lhadj

Student:

Fatima Ait-Mahammed;Keyur Patel

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Concordia University