2020•Unpublished venueOpen access

Automation of Datamorphic Testing

Hong Zhu, Ian Bayley, Dongmei Liu, Xiaoyu Zheng

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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. This paper proposes a set of test strategies that combine datamorphisms to generate test sets that adequately cover various types of mutant test cases. These strategies are formally defined. Their implementation algorithms are provided. The correctness of the algorithms are proved. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.

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What this paper is about

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. This paper proposes a set of test strategies that combine datamorphisms to generate test sets that adequately cover various types of mutant test cases. These strategies are formally defined. Their implementation algorithms are provided. The correctness of the algorithms are proved. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.

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Available 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. This paper proposes a set of test strategies that combine datamorphisms to generate test sets that adequately cover various types of mutant test cases. These strategies are formally defined. Their implementation algorithms are provided. The correctness of the algorithms are proved. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.

Key concepts: Test Management Approach, Test harness, Test script, Computer science, System under test, Test (biology), Test case, Keyword-driven testing

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