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 paper, 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)1-15
Number of pages15
JournalIEEE Transactions on Software Engineering
Publication statusPublished - 12 Sept 2023


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