  Modern application development allows applications to be composed using
lightweight HTTP services. Testing such an application requires the
availability of services that the application makes requests to. However,
access to dependent services during testing may be restrained. Simulating the
behaviour of such services is, therefore, useful to address their absence and
move on application testing. This paper examines the appropriateness of
Symbolic Machine Learning algorithms to automatically synthesise HTTP services'
mock skeletons from network traffic recordings. These skeletons can then be
customised to create mocks that can generate service responses suitable for
testing. The mock skeletons have human-readable logic for key aspects of
service responses, such as headers and status codes, and are highly accurate.
