QuickCheck

Claessen, Koen; Hughes, John

doi:10.1145/351240.351266

Cited by 658 publications

(81 citation statements)

References 12 publications

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“…QuickCheck [Claessen and Hughes 2000] is a tool that combines random data generation with a property language in order to test our code more thoroughly. QuickCheck testing is accomplished via a combination of a language to describe properties and a language to generate input data.…”

Section: Quickcheckingmentioning

confidence: 99%

Testing and debugging functional reactive programming

Perez

Nilsson

2017

Proc. ACM Program. Lang.

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Many types of interactive applications, including video games, raise particular challenges when it comes to testing and debugging. Reasons include de-facto lack of reproducibility and difficulties of automatically generating suitable test data. This paper demonstrates that certain variants of Functional Reactive Programming (FRP) implemented in pure functional languages can mitigate such difficulties by offering referential transparency at the level of whole programs. This opens up for a multi-pronged approach for assisting with testing and debugging that works across platforms, including assertions based on temporal logic, recording and replaying of runs (also from deployed code), and automated random testing using QuickCheck. The approach has been validated on real, non-trivial games implemented in the FRP system Yampa through a tool providing a convenient Graphical User Interface that allows the execution of the code under scrutiny to be controlled, moving along the execution time line, and pin-pointing of violations of assertions on PCs as well as mobile platforms.

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Section: Quickcheckingmentioning

confidence: 99%

Testing and debugging functional reactive programming

Perez

Nilsson

2017

Proc. ACM Program. Lang.

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“…The first PBT tool was QuickCheck [8] for Haskell. There are many other tools that are based on the concepts of QuickCheck, e.g.…”

Section: ∀Xs ∈ List[t ] : Reverse(reverse(xs)) = Xsmentioning

confidence: 99%

“…Property-based testing (PBT) is a testing technique that tries to falsify a given property by generating random input data and verifying the expected behaviour [8]. Properties can range from simple algebraic equations to complex state machine models.…”

Section: Introductionmentioning

confidence: 99%

“…Like in all model-based testing techniques, the properties serve as a source for test case generation as well as test oracles. PBT is a well-known testing practice in functional programming [3,8,9], but nowadays we see a growth of applications outside its traditional domain. Examples include the automated testing of automotive software [4,30], cloud computing [16] and web services [11,18].…”

Section: Introductionmentioning

confidence: 99%

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Property-based testing of web services by deriving properties from business-rule models

Aichernig

Schumi

2017

Softw Syst Model

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Property-based testing is well suited for web-service applications, which was already shown in various case studies. For example, it has been demonstrated that JSON schemas can be used to automatically derive test case generators for web forms. In this work, we present a test case generation approach for a rule engine-driven web-service application. Business-rule models serve us as input for property-based testing. We parse these models to automatically derive generators for sequences of web-service requests together with their required form data. Property-based testing is mostly applied in the context of functional programming. Here, we define our properties in an object-oriented style in C# and its tool FsCheck. We apply our method to the business-rule models of an industrial web-service application in the automotive domain.

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“…The QuickCheck library [CH00] for Haskell, a purely functional programming language, enables the statement of properties of program constructs, which are then automatically tested. Test cases are randomly generated from the types of the variables in properties.…”

Section: Related Workmentioning

confidence: 99%

JSConTest: Contract-Driven Testing and Path Effect Inference for JavaScript.

Heidegger¹,

Thiemann²

2012

JOT

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Program understanding is a major obstacle during program maintenance. In an object-oriented language, understanding an operation requires understanding its type and its effect on the object network. The effect is particularly important for scripting languages where there is neither class structure that restricts the shape of an object nor any other kind of access control.We have designed and implemented JSConTest, a tool that provides a facility to annotate JavaScript programs with type and effect contracts and to create random tests out of the contracts. Run-time monitoring for contracts is implemented with a program transformation. The effect of an operation is described by access permissions, which abstract sets of access paths along which the operation reads or writes object properties. Type contracts can also be used to drive guided random testing of the program.JSConTest contains an algorithm for computing access permissions from a set of access paths obtained by running the program. The main ingredient of the algorithm is a novel heuristic that produces precise and concise results without user interaction. It has been applied to a range of examples with encouraging results.

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QuickCheck

Cited by 658 publications

References 12 publications

Testing and debugging functional reactive programming

Testing and debugging functional reactive programming

Property-based testing of web services by deriving properties from business-rule models

JSConTest: Contract-Driven Testing and Path Effect Inference for JavaScript.

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