Morphy: A Datamorphic Software Test Automation Tool

TL;DR

Morphy is an automated datamorphic testing tool that classifies test artefacts and employs various test morphisms, enabling automation of testing activities, strategies, and processes for traditional and AI software through case studies.

Contribution

The paper introduces Morphy, a novel automated tool that utilizes datamorphic testing concepts, including new strategies and algorithms for test automation and optimization.

Findings

Successfully automates basic testing activities.

Implements complex test strategies using test morphisms.

Demonstrates effectiveness on traditional and AI software through case studies.

Abstract

This paper presents an automated tool called Morphy for datamorphic testing. It classifies software test artefacts into test entities and test morphisms, which are mappings on testing entities. In addition to datamorphisms, metamorphisms and seed test case makers, Morphy also employs a set of other test morphisms including test case metrics and filters, test set metrics and filters, test result analysers and test executers to realise test automation. In particular, basic testing activities can be automated by invoking test morphisms. Test strategies can be realised as complex combinations of test morphisms. Test processes can be automated by recording, editing and playing test scripts that invoke test morphisms and strategies. Three types of test strategies have been implemented in Morphy: datamorphism combination strategies, cluster border exploration strategies and strategies for test set optimisation via genetic algorithms. This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.