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QVM: An Efficient Runtime for Detecting Defects in Deployed Systems
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BibTeX
@MISC{Arnold_qvm:an,
author = {Matthew Arnold and Martin Vechev and Eran Yahav},
title = {QVM: An Efficient Runtime for Detecting Defects in Deployed Systems},
year = {}
}
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Abstract
Coping with software defects that occur in the post-deployment stage is a challenging problem: bugs may occur only when the system uses a specific configuration and only under certain usage scenarios. Nevertheless, halting production systems until the bug is tracked and fixed is often impossible. Thus, developers have to try to reproduce the bug in laboratory conditions. Often the reproduction of the bug consists of the lion share of the debugging effort. In this paper we suggest an approach to address the aforementioned problem by using a specialized runtime environment (QVM, for Quality Virtual Machine). QVM efficiently detects defects by continuously monitoring the execution of the application in a production setting. QVM enables the efficient checking of violations of user-specified correctness properties, e.g., typestate safety properties, Java assertions, and heap properties pertaining to ownership. QVM is markedly different from existing techniques for continuous monitoring by using a novel overhead manager which enforces a user-specified overhead budget for quality checks. Existing tools for error detection in the field usually disrupt the operation of the deployed system. QVM, on the other hand, provides a balanced trade off between the cost of the monitoring process and the maintenance of sufficient accuracy for detecting defects. Specifically, the overhead cost of using QVM instead of a standard JVM, is low enough to be acceptable in production environments. We implemented QVM on top of IBM’s J9 Java Virtual Machine and used it to detect and fix various errors in realworld applications.
Keyphrases
deployed system efficient runtime heap property sufficient accuracy software defect overhead cost realworld application specific configuration production environment novel overhead manager quality virtual machine error detection typestate safety property monitoring process balanced trade lion share ibm j9 java virtual machine certain usage scenario laboratory condition continuous monitoring user-specified overhead budget quality check specialized runtime environment existing tool aforementioned problem user-specified correctness property standard jvm post-deployment stage java assertion production setting production system various error efficient checking
