Improving the Logging Practices in DevOps

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. In this thesis, we first conduct a systematic survey on the instrumentation techniques used in software logging. Then we propose 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. In particular, we study historical issues in logging code and their fixes from six popular Java-based open source projects. We found that existing state-of-the-art techniques on detecting logging code issues cannot detect a majority of the issues in logging code. We also study the use of Java logging utilities in the wild. We find the complexity of the use of logging utilities increases as the project size increases. We aim to support the IT operation side by enriching the log analysis context. In particular, we propose a technique, LogCoCo, to systematically estimate code coverage via executing logs. The results of LogCoCo are highly accurate under a variety of testing activities. Case studies show that our techniques and findings can provide useful software logging suggestions to both developers and operators in open source and commercial systems