Rex: Preventing Bugs and Misconfiguration in Large Services using Correlated Change Analysis
Large services experience extremely frequent changes to code
and configuration. In many cases, these changes are correlated
across files. For example, an engineer introduces a new
feature following which they also change a configuration file
to enable the feature only on a small number of experimental
machines. This example captures only one of numerous
types of correlations that emerge organically in large services.
Unfortunately, in almost all such cases, no documentation
or specification guides engineers on how to make correlated
changes and they often miss making them. Such misses can
be vastly disruptive to the service.
We have designed and deployed Rex, a tool that, using
a combination of machine-learning and program analysis,
learns change-rules that capture such correlations. When
an engineer changes only a subset of files in a change-rule,
Rex suggests additional changes to the engineer based on the
change-rule. Rex has been deployed for 14 months on 360
repositories within Microsoft that hold code and configuration
for services such as Office 365 and Azure. Rex has so far
positively affected 4926 changes without which, at the very
least, code-quality would have degraded and, in some cases,
the service would have been severely disrupted.