Multi-misconfiguration Diagnosis via Identifying Correlated Configuration Parameters

Yingnan Zhou, Xue Hu, Sihan Xu, Yan Jia, Yuhao Liu, Junyong Wang, Guangquan Xu, Wei Wang, Shaoying Liu, Thar Baker

Research output: Contribution to journalArticlepeer-review


Software configuration requires that the user sets appropriate values to specified variables, known as configuration parameters, which potentially affect the behaviors of software system. It is an essential means for software reliability, but how to ensure correct configurations remains a great challenge, especially when a large number of parameter settings are involved. Existing studies on misconfiguration diagnosis treat all configurations independently, ignoring the constraints and correlations among different configurations. In this article, we reveal the phenomenon of multi-misconfigurations and present a tool, MMD, for multi-misconfigurations diagnosis. Specifically, MMD consists of two modules: Correlated Configurations Analysis and Primary Misconfigurations Diagnosis. The former determines the correlation among each pair of configurations by analyzing the control and data flows related to each configuration. The latter is responsible for collecting a list of configurations ranked according to their suspiciousness. Combining the outputs of two modules, MMD is able to assist the user in multi-misconfigurations diagnosis. We evaluate MMD on seven popular Java projects: Randoop, Soot, Synoptic, Hdfs, Hbase, Yarn, and Zookeeper. MMD identifies 510 configuration correlations with a 4.9% false positive rate. Furthermore, it effectively diagnoses 22 multi-misconfigurations collected from StackOverflow, outperforming two state-of-the-art baselines.

Original languageEnglish
Pages (from-to)4624-4638
Number of pages15
JournalIEEE Transactions on Software Engineering
Issue number10
Publication statusPublished - 12 Sept 2023

Bibliographical note

Funding Information:
This work was supported in part by the Fundamental Research Funds for the Central Universities of China under Grant 2020 JBZ104; in part by the National Natural Science Foundation of China under Grants U21A20463, U22B2027, 62172297, 62202245, 62102198, 61971029, and 61902276; in part by Tianjin Intelligent Manufacturing Special Fund Project under Grant 20211097; and in part by China Postdoctoral Science Foundation under Grants 2021M691673 and 2023T160335.

Publisher Copyright:
© 1976-2012 IEEE.


  • Behavioral sciences
  • Codes
  • Configuration
  • Correlation
  • Manuals
  • Software
  • Source coding
  • Yarn
  • correlation
  • diagnosis
  • multi-misconfiguration
  • parameters


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