Sentomist: Unveiling Transient Sensor Network Bugs via Symptom Mining

摘要

Wireless Sensor Network (WSN) applications are typically event-driven. While the source codes of these applications may look simple, they are executed with a complicated concurrency model, which frequently introduces software bugs, in particular, transient bugs. Such buggy logics may only be triggered by some occasionally interleaved events that bear implicit dependency, but can lead to fatal system failures. Unfortunately, these deeply-hidden bugs or even their symptoms can hardly be identified by state-of-the-art debugging tools, and manual identification from massive running traces can be prohibitively expensive. In this paper, we present Sentomist (Sensor application anatomist), a novel tool for identifying potential transient bugs in WSN applications. The Sentomist design is based on a key observation that transient bugs make the behaviors of a WSN system deviate from the normal, and thus outliers (i.e., abnormal behaviors) are good indicators of potential bugs. Sentomist introduces the notion of event-handling interval to systematically anatomize the long-term execution history of an event-driven WSN system into groups of intervals. It then applies a customized outlier detection algorithm to quickly identify and rank abnormal intervals. This dramatically reduces the human efforts of inspection (otherwise, we have to manually check tremendous data samples, typically with brute force inspection) and thus greatly speeds up debugging. We have implemented Sentomist based on the concurrency model of TinyOS. We apply Sentomist to test a series of representative real-life WSN applications that contain transient bugs. These bugs, though caused by complicated interactions that can hardly be predicted during the programming stage, are successfully confined by Sentomist.