Monic Testing of Web Services Based on Algebraic Specifications

Li, Dongmei; Wu, Xian; Zhang, Xin; Zhu, Hong; Bayley, Ian

doi:10.1109/sose.2016.45

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Another advantage of algebraic specification is that it enables automated testing of software against formal specifications [23], [24]. These test automation techniques can be applied to the implementation of system security design formally specified in algebraic specifications.…”

Section: Discussionmentioning

confidence: 99%

Pattern-Based Approach to Modelling and Verifying System Security

Zheng

Zhu

et al. 2020

2020 IEEE International Conference on Service Oriented Systems Engineering (SOSE)

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Security is one of the most important problems in the engineering of online service-oriented systems. The current best practice in security design is a pattern-oriented approach. A large number of security design patterns have been identified, categorised and documented in the literature. The design of a security solution for a system starts with identification of security requirements and selection of appropriate security design patterns; these are then composed together. It is crucial to verify that the composition of security design patterns is valid in the sense that it preserves the features, semantics and soundness of the patterns and correct in the sense that the security requirements are met by the design. This paper proposes a methodology that employs the algebraic specification language SOFIA to specify security design patterns and their compositions. The specifications are then translated into the Alloy formalism and their validity and correctness are verified using the Alloy model checker. A tool that translates SOFIA into Alloy is presented. A case study with the method and the tool is also reported.

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Section: Discussionmentioning

confidence: 99%

Pattern-Based Approach to Modelling and Verifying System Security

Zheng

Zhu

et al. 2020

2020 IEEE International Conference on Service Oriented Systems Engineering (SOSE)

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“…Algebraic speci cations have been used for test automation since the early 1980s. ey have been developed for testing procedural programs (Gonnon et al, 1981;Bernot et al, 1991), object oriented programs (Doong and Frankl, 1994;Hughes and Sto s, 1996;Chen et al, 2001aChen et al, , 1998a, component-based systems (Kong et al, 2007;Yu et al, 2008), and more recently for service-oriented systems (Liu et al, 2016). e research on metamorphic testing demonstrated that such axioms can be useful for testing so ware even if they do not form a complete set of axioms, though the la er are o en required by test tools that automate so ware testing from algebraic speci cations (Chen et al, 2001a(Chen et al, , 1998a.…”

Section: Metamorphic Testingmentioning

confidence: 99%

Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Zhu¹,

Bayley²

2021

Preprint

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Testing has been widely recognised as di cult for AI applications. is paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. In these strategies, testing aims at exploring the data space of a classi cation or clustering application to discover the boundaries between classes that the machine learning application de nes.is enables the tester to understand precisely the behaviour and function of the so ware under test. In the paper, three variants of exploratory strategies are presented with the algorithms implemented in the automated datamorphic testing tool Morphy. e correctness of these algorithms are formally proved. eir capability and cost of discovering borders between classes are evaluated via a set of controlled experiments with manually designed subjects and a set of case studies with real machine learning models.

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“…For example, test case generation techniques and tools [1] like fuzz testing [25], data mutation testing [22], random testing [2], adaptive random testing [8,17], combinatorial testing [21] and model based test case generators are all test morphisms, which can be wrapped as seed makers or datamorphisms. Metamorphic relations in metamorphic testing [7] and formal specification-based test oracles [4,5,6,28,15,16] are metamorphisms. Test coverage measurement tools like [24] are test set metrics.…”

Section: Related Workmentioning

confidence: 99%

Automation of Datamorphic Testing

Zhu

Bayley

et al. 2020

2020 IEEE International Conference on Artificial Intelligence Testing (AITest)

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This paper presents an automated tool called M orphy 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.

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Monic Testing of Web Services Based on Algebraic Specifications

Cited by 5 publications

References 32 publications

Pattern-Based Approach to Modelling and Verifying System Security

Pattern-Based Approach to Modelling and Verifying System Security

Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Automation of Datamorphic Testing

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