Detecting misconfiguration and/or bug(s) in large service(s) using correlated change analysis
Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configu...
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| creator | Mehta, Sonu Bhagwan, Ranjita Kumar, Aditya Bansal, Chetan Bird, Christian Alma Ashok, Balasubramanyan Asthana, Sumit Maddila, Chandra Sekhar Kumar, Rahul |
| description | Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configuration files using a machine learning algorithm that discovers change rules using a support parameter, and, a confidence parameter, and, a refinement algorithm that refines the discovered change rules. The correlated change model comprising the change rules is stored. The correlated change model can be used to identify potential issue(s) regarding a particular file (e.g., changed code or configuration file(s)). Information regarding the identified potential issue(s) can be provided to a user. |
| format | Patent |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Detecting misconfiguration and/or bug(s) in large service(s) using correlated change analysis |
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