Generating Mock Skeletons for Lightweight Web-Service Testing
This addresses the issue of restricted access to dependent services during testing for developers, though it appears incremental as it builds on existing symbolic methods for mock generation.
The paper tackles the problem of testing applications that rely on HTTP services by proposing a method to automatically generate mock skeletons from network traffic recordings using Symbolic Machine Learning algorithms, achieving highly accurate results.
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.