Fragmented Monitoring

TL;DR

Fragmented monitoring is a technique that records partial traces with annotations to reduce overhead and reconstruct likely full traces offline, enabling efficient field data collection without impacting user experience.

Contribution

The paper introduces fragmented monitoring, a novel approach that records partial traces with annotations to infer complete behavior, balancing data collection quality and overhead.

Findings

Reduces monitoring overhead significantly.

Enables reconstruction of likely full traces offline.

Maintains comprehensive behavior insights with less resource use.

Abstract

Field data is an invaluable source of information for testers and developers because it witnesses how software systems operate in real environments, capturing scenarios and configurations relevant to end-users. Unfortunately, collecting traces might be resource-consuming and can significantly affect the user experience, for instance causing annoying slowdowns. Existing monitoring techniques can control the overhead introduced in the applications by reducing the amount of collected data, for instance by collecting each event only with a given probability. However, collecting fewer events limits the amount of information extracted from the field and may fail in providing a comprehensive picture of the behavior of a program. In this paper we present fragmented monitoring, a monitoring technique that addresses the issue of collecting information from the field without annoying users. The key idea of fragmented monitoring is to reduce the overhead by recording partial traces (fragments) instead of full traces, while annotating the beginning and the end of each fragment with state information. These annotations are exploited offline to derive traces that might be likely observed in the field and that could not be collected directly due to the overhead that would be introduced in a program.