skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Static detection of silent misconfigurations with deep interaction analysis
The behavior of large systems is guided by their configurations: users set parameters in the configuration file to dictate which corresponding part of the system code is executed. However, it is often the case that, although some parameters are set in the configuration file, they do not influence the system runtime behavior, thus failing to meet the user’s intent. Moreover, such misconfigurations rarely lead to an error message or raising an exception. We introduce the notion of silent misconfigurations which are prohibitively hard to identify due to (1) lack of feedback and (2) complex interactions between configurations and code. This paper presents ConfigX, the first tool for the detection of silent misconfigurations. The main challenge is to understand the complex interactions between configurations and the code that they affected. Our goal is to derive a specification describing non-trivial interactions between the configuration parameters that lead to silent misconfigurations. To this end, ConfigX uses static analysis to determine which parts of the system code are associated with configuration parameters. ConfigX then infers the connections between configuration parameters by analyzing their associated code blocks. We design customized control- and data-flow analysis to derive a specification of configurations. Additionally, we conduct reachability analysis to eliminate spurious rules to reduce false positives. Upon evaluation on five real-world datasets across three widely-used systems, Apache, vsftpd, and PostgreSQL, ConfigX detected more than 2200 silent misconfigurations. We additionally conducted a user study where we ran ConfigX on misconfigurations reported on user forums by real-world users. ConfigX easily detected issues and suggested repairs for those misconfigurations. Our solutions were accepted and confirmed in the interaction with the users, who originally posted the problems.  more » « less
Award ID(s):
1715387
PAR ID:
10311545
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
5
Issue:
OOPSLA
ISSN:
2475-1421
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation)
    null (Ed.)
    Misconfiguration is a major cause of system failures. Prior solutions focus on detecting invalid settings that are introduced by user mistakes. But another type of misconfiguration that continues to haunt production services is specious configuration---settings that are valid but lead to unexpectedly poor performance in production. Such misconfigurations are subtle, so even careful administrators may fail to foresee them. We propose a tool called Violet to detect specious configuration. We realize the crux of specious configuration is that it causes some slow code path to be executed, but the bad performance effect cannot always be triggered. Violet thus takes a novel approach that uses selective symbolic execution to systematically reason about the performance effect of configuration parameters, their combination effect, and the relationship with input. Violet outputs a performance impact model for the automatic detection of poor configuration settings. We applied Violet on four large systems. To evaluate the effectiveness of Violet, we collect 17 real-world specious configuration cases. Violet detects 15 of them. Violet also identifies 11 unknown specious configurations. 
    more » « less
  2. ; ; ( , 14th USENIX Symposium on Operating Systems Design and Implementation)
    null (Ed.)
    Misconfiguration is a major cause of system failures. Prior solutions focus on detecting invalid settings that are introduced by user mistakes. But another type of misconfiguration that continues to haunt production services is specious configuration—settings that are valid but lead to unexpectedly poor performance in production. Such misconfigurations are subtle, so even careful administrators may fail to foresee them. We propose a tool called Violet to detect specious configuration. We realize the crux of specious configuration is that it causes some slow code path to be executed, but the bad performance effect cannot always be triggered. Violet thus takes a novel approach that uses selective symbolic execution to systematically reason about the performance effect of configuration parameters, their combination effect, and the relationship with input. Violet outputs a performance impact model for the automatic detection of poor configuration settings. We applied Violet on four large systems. To evaluate the effectiveness of Violet, we collect 17 real-world specious configuration cases. Violet detects 15 of them. Violet also identifies 11 unknown specious configurations 
    more » « less
  3. ; ; ; ; ( , 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER))
    Continuous Integration (CI) allows developers to check whether their code can build successfully and pass tests across various system environments with every commit. To use a CI platform, a developer must provide configuration files within a code repository to specify build conditions. Incorrect configuration settings lead to CI build failures, which can take hours to run, wasting valuable developer time and delaying product release dates. Debugging CI configurations is a slow and error-prone process. The only way to check the correctness of CI configurations is to push a commit and wait for the build result. We present VeriCI, the first system for localizing CI configuration errors at the code level. VeriCI runs as a static analysis tool, before the developer sends the build request to the CI server. Our key insight is that the commit history and the corresponding build histories available in CI environments can be used both for build error prediction and build error localization. We leverage the build history as a labeled dataset to automatically derive customized rules describing correct CI configurations, using supervised machine learning techniques. To more accurately identify root causes, we train a neural network that filters out constraints that are less likely to be connected to the root cause of build failure. We evaluate VeriCI on real world data from GitHub and achieve 91% accuracy of predicting a build failure and correctly identify the root cause in 75% of cases. We also conducted a between-subjects user study with 20 software developers, showing that VeriCI significantly helps users in identifying and fixing errors in CI. 
    more » « less
  4. ; ; ; ; ; ( , In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI'20))
    null (Ed.)
    Large-scale cloud services deploy hundreds of configuration changes to production systems daily. At such velocity, con- figuration changes have inevitably become prevalent causes of production failures. Existing misconfiguration detection and configuration validation techniques only check configu- ration values. These techniques cannot detect common types of failure-inducing configuration changes, such as those that cause code to fail or those that violate hidden constraints. We present ctests, a new type of tests for detecting failure- inducing configuration changes to prevent production failures. The idea behind ctests is simple—connecting production sys- tem configurations to software tests so that configuration changes can be tested in the context of code affected by the changes. So, ctests can detect configuration changes that ex- pose dormant software bugs and diverse misconfigurations. We show how to generate ctests by transforming the many existing tests in mature systems. The key challenge that we address is the automated identification of test logic and oracles that can be reused in ctests. We generated thousands of ctests from the existing tests in five cloud systems. Our results show that ctests are effective in detecting failure-inducing configuration changes before deployment. We evaluate ctests on real-world failure-inducing configura- tion changes, injected misconfigurations, and deployed con- figuration files from public Docker images. Ctests effectively detect real-world failure-inducing configuration changes and misconfigurations in the deployed files. 
    more » « less
  5. ; ; ; ( , ACM SIGSOFT International Symposium on Software Testing and Analysis)
    Configuration changes are among the dominant causes of failures of large-scale software system deployment. Given the velocity of configuration changes, typically at the scale of hundreds to thousands of times daily in modern cloud systems, checking these configuration changes is critical to prevent failures due to misconfigurations. Recent work has proposed configuration testing, Ctest, a technique that tests configuration changes together with the code that uses the changed configurations. Ctest can automatically generate a large number of ctests that can effectively detect misconfigurations, including those that are hard to detect by traditional techniques. However, running ctests can take a long time to detect misconfigurations. Inspired by traditional test-case prioritization (TCP) that aims to reorder test executions to speed up detection of regression code faults, we propose to apply TCP to reorder ctests to speed up detection of misconfigurations. We extensively evaluate a total of 84 traditional and novel ctest-specific TCP techniques. The experimental results on five widely used cloud projects demonstrate that TCP can substantially speed up misconfiguration detection. Our study provides guidelines for applying TCP to configuration testing in practice. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email