Generating Mock Skeletons for Lightweight Web-Service Testing

TL;DR

This paper explores using Symbolic Machine Learning algorithms to automatically generate human-readable, customizable mock skeletons of HTTP services from network traffic, aiding testing when dependent services are unavailable.

Contribution

It introduces a novel approach to synthesize accurate, customizable mock skeletons of HTTP services using symbolic machine learning from network traffic recordings.

Findings

Mock skeletons are highly accurate in representing service responses.

Generated skeletons include human-readable logic for headers and status codes.

Approach facilitates testing without access to real dependent services.

Abstract

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.