Understanding Log Lines Using Development Knowledge

Logs are generated by output statements that developers insert into the code. By recording the system behaviour during runtime, logs play an important role in the maintenance of large software systems. The rich nature of logs has introduced a new market of log management applications (e.g., Splunk, XpoLog and log stash) that assist in storing, querying and analyzing logs. Moreover, recent research has demonstrated the importance of logs in operating, understanding and improving software systems. Thus log maintenance is an important task for the developers. However, all too often practitioners (i.e., operators and administrators) are left without any support to help them unravel the meaning and impact of specific log lines. By spending over 100 human hours and manually examining all the email threads in the mailing list for three open source systems (Hadoop, Cassandra and Zookeeper) and performing web search on sampled logging statements, we found 15 email inquiries and 73 inquiries from web search about different log lines. We identified that five types of development knowledge that are often sought from the logs by practitioners: meaning, cause, context, impact and solution. Due to the frequency and nature of log lines about which real customers inquire, documenting all the log lines or identifying which ones to document is not efficient. Hence in this paper we propose an on-demand approach, which associates the development knowledge present in various development repositories (e.g., code commits and issues reports) with the log lines. Our case studies show that the derived development knowledge can be used to resolve real-life inquiries about logs.