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Systems can become misconfigured for a variety of reasons such as operatorerrors or buggy patches. When a misconfiguration is discovered, usually thefirst order of business is to restore availability, often by undoing themisconfiguration. To simplify this task, we propose the Statistical Analysisfor Identifying Configuration Files (SAIC), which analyzes how the contents ofa file changes over time to automatically determine which files containconfiguration state. In this way, SAIC reduces the number of files a user mustmanually examine during recovery and allows versioning file systems to makemore efficient use of their versioning storage.  The two key insights that enable SAIC to identify configuration files arethat configuration state must persist across executions of an application andthat configuration state changes at a slower rate than other types ofapplication state. SAIC applies these insights through a set of filters, whicheliminate non-persistent files from consideration, and a novel similaritymetric, which measures how similar a file's versions are to each other.Together, these two mechanisms enable SAIC to identify all 72 configurationfiles out of 2363 versioned files from 6 common applications in two usertraces, while mistaking only 33 non-configuration files as configuration files,which allows a versioning file system to eliminate roughly 66% ofnon-configuration file versions from its logs, thus reducing the number of fileversions that a user must try to recover from a misconfiguration.

SAIC: Identifying Configuration Files for System Configuration Management