Morphy: A Datamorphic Software Test Automation Tool
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023A Scenario-Based Functional Testing Approach to Improving DNN Performancecited by 7
- 2024ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generationcited by 2
- 2021Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testingcited by 1
- 2026Operational Robustness of LLMs on Code Generationcited by 0
- 2026Operational Robustness of LLMs on Code Generation21 Feb 2026
- 2024ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation18 Jun 2024
- 2023A Scenario-Based Functional Testing Approach to Improving DNN Performance13 Jul 2023
- 2021Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing1 Oct 2021