Gathering operational data about a system is common practice, particularly metrics that indicate system load and performance such as CPU and memory usage. This data has been used for years to help teams who support a system learn when an outage is happening or imminent. When things become slow, a code profiler might be enabled in order to determine which part of the system is causing a bottleneck, for example a slow-running database query.
I’ve observed a recent trend that combines the meticulousness of this traditional operational monitoring with a much broader view of the quality of a system. While operational data is an essential part of supporting a system, it is also valuable to gather data that helps provide a picture of whether the system as a whole is behaving as expected. I define “QA in production” as an approach where teams pay closer attention to the behaviour of their production systems in order to improve the overall quality of the function these systems serve.
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