Logs are valuable for failure diagnosis and software debugging in practice. However, due to the ad-hoc style of inserting logging statements, the quality of logs can hardly be guaranteed. In case of a system failure, the log file may contain a large number of irrelevant logs, while crucial clues to the root cause may still be missing.
In this paper, we present an automated approach to log improvement based on the combination of information from program source code and textual logs. It selects the most relevant ones from an ocean of logs to help developers focus and reason along the causality chain, and generates additional informative logs to help developers discover the root causes of failures. We have conducted a preliminary case study using an implementation prototype to demonstrate the usefulness of our approach.