Log4Perf

Abstract

Performance assurance activities are an essential step in the release cycle of software systems. Logs have become one of the most important sources of information that is used to monitor, understand and improve software performance. However, developers often face the challenge of making logging decisions, i.e., neither logging too little and logging too much is desirable. Although prior research has proposed techniques to assist in logging decisions, those automated logging guidance techniques are rather general, without considering a particular goal, such as monitoring software performance. In this paper, we present Log4Perf, an automated approach that provides suggestions of where to insert logging statement with the goal of monitoring web-based systems» software performance. In particular, our approach builds and manipulates a statistical performance model to identify the locations in the source code that statistically significantly influences software performance. To evaluate Log4Perf, we conduct case studies on open source system, i.e., CloudStore and OpenMRS, and one large-scale commercial system. Our evaluation results show that Log4Perf can build well-fit statistical performance models, indicating that such models can be leveraged to investigate the influence of locations in the source code on performance. Also, the suggested logging locations are often small and simple methods that do not have logging statements and that are not performance hotspots, making our approach an ideal complement to traditional approaches that are based on software metrics or performance hotspots. Log4Perf is integrated into the release engineering process of the commercial software to provide logging suggestions on a regular basis.