MuCheck: an extensible tool for mutation testing of haskell programs

Le, Duc Minh; Alipour, Mohammad Amin; Gopinath, Rahul; Groce, Alex

doi:10.1145/2610384.2628052

Cited by 27 publications

(18 citation statements)

References 12 publications

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“…The method has been applied on the most popular programming languages such as C [6], C++ [7], C# [8], Java [9], JavaScript [10], Ruby [11] including specification [2] and modelling languages [12]. It has also been adapted for the most popular programming paradigms such as Object-Oriented [13], Functional [14], aspect-oriented and declarative-oriented [15,16] programming.…”

Section: Introductionmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

345

343

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Mutation testing realises the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases and to support experimentation.Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a 'mini-handbook'-style roadmap for the application of mutation testing as experimental methodology.

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Section: Introductionmentioning

confidence: 99%

Mutation Testing Advances: An Analysis and Survey

Papadakis

Kintis

Zhang

et al. 2019

Advances in Computers

345

343

View full text Add to dashboard Cite

show abstract

“…Imperative languages aside, mutation testing has been considered for declarative and functional languages as well [1]. Usable tools exist, for instance, for Haskell [11].…”

Section: Related Workmentioning

confidence: 99%

Measuring Coverage of Prolog Programs Using Mutation Testing

Efremidis

Schmidt

Krings

et al. 2019

Functional and Constraint Logic Programming

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Testing is an important aspect in professional software development, both to avoid and identify bugs as well as to increase maintainability. However, increasing the number of tests beyond a reasonable amount hinders development progress. To decide on the completeness of a test suite, many approaches to assert test coverage have been suggested. Yet, frameworks for logic programs remain scarce. In this paper, we introduce a framework for Prolog programs measuring test coverage using mutations. We elaborate the main ideas of mutation testing and transfer them to logic programs. To do so, we discuss the usefulness of different mutations in the context of Prolog and empirically evaluate them in a new mutation testing framework on different examples.

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“…Consider the following explicit definition of sort, which is used as an example in Le et al (2014). Given this definition, and properties 1-6 listed in §5.2, MuCheck with default settings gives the following output.…”

Section: Sorting ( §52)mentioning

confidence: 99%

“…The competent programmer hypothesis (Demillo et al 1978) states: "[Competent programmers] create programs that are close to being correct". In mutation-testing literature, mostly concerned with imperative languages (Jia and Harman 2011;Le et al 2014), closeness is usually regarded as syntactic closeness. We suggest that a semantic notion of closeness is even more suitable for pure strongly-typed functional programs: minor syntactic slips are very often caught by the type-checker; errors that are harder to detect involve incorrect associations between input and output values.…”

Section: Sorting ( §52)mentioning

confidence: 99%

FitSpec: refining property sets for functional testing

Braquehais

Runciman

2016

Proceedings of the 9th International Symposium on Haskell

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This paper presents FitSpec, a tool providing automated assistance in the task of refining sets of test properties for Haskell functions. FitSpec tests mutant variations of functions under test against a given property set, recording any surviving mutants that pass all tests. The number of surviving mutants and any smallest survivor are presented to the user. A surviving mutant indicates incompleteness of the property set, prompting the user to amend a property or to add a new one, making the property set stronger. Based on the same test results, FitSpec also provides conjectures in the form of equivalences and implications between property subsets. These conjectures help the user to identify minimal core subsets of properties and so to reduce the cost of future property-based testing.

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MuCheck: an extensible tool for mutation testing of haskell programs

Cited by 27 publications

References 12 publications

Mutation Testing Advances: An Analysis and Survey

Mutation Testing Advances: An Analysis and Survey

Measuring Coverage of Prolog Programs Using Mutation Testing

FitSpec: refining property sets for functional testing

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