Abstract: A machine may be configured to determine correlations between anomalies in operational metrics and software deployments. For example, the machine, based on an anomaly pertaining to the running of the service, accesses an operational metric associated with a service running on a social networking service (SNS). The machine generates, based on a log file associated with a software deployment, a deployment time series of completion percentage values indicating levels of completion of the software deployment. The machine determines that a correlation exists between the operational metric and the deployment time series of completion percentage values, and generates a correlation strength identifier based on a determination of a strength of the correlation. The correlation strength identifier indicates a likelihood that the software deployment has caused the anomaly. The machine causes a presentation of the correlation strength identifier in a user interface of a client device.

Abstract: A machine may be configured to determine correlations between anomalies in operational metrics and software deployments. For example, the machine, based on an anomaly pertaining to the running of the service, accesses an operational metric associated with a service running on a social networking service (SNS). The machine generates, based on a log file associated with a software deployment, a deployment time series of completion percentage values indicating levels of completion of the software deployment. The machine determines that a correlation exists between the operational metric and the deployment time series of completion percentage values, and generates a correlation strength identifier based on a determination of a strength of the correlation. The correlation strength identifier indicates a likelihood that the software deployment has caused the anomaly. The machine causes a presentation of the correlation strength identifier in a user interface of a client device.