DevOps refers to a set of practices dedicated to accelerating modern software engineering process. It breaks the barriers between software development and IT operations and aims to produce and maintain high quality software systems. Software logging is widely used in DevOps. However, there are few guidelines and tool support for composing high quality logging code and current application context of log analysis is very limited with respect to feedback for developers and correlations among other telemetry data. This thesis proposes automated approaches to improving software logging practices in DevOps by leveraging various types of software repositories (e.g., historical, communication, bug, and runtime repositories). We aim to support the software development side by providing guidelines and tools on developing and maintaining high quality logging code. We aim to support the IT operation side by enriching the log analysis context through systematic estimating code coverage via executing logs and in-depth problem diagnosis by correlating logs with other telemetry data (e.g., traces and APM data). Case studies show that our approaches can provide useful software logging suggestions to both developers and operators in open source and commercial systems.