EasyCheck — Test Data for Free

Christiansen, Jan; Fischer, Sebastian

doi:10.1007/978-3-540-78969-7_23

Cited by 44 publications

(50 citation statements)

References 14 publications

(15 reference statements)

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“…Berghofer and Nipkow (2004) proposed a QuickCheck-like tool for Isabelle/HOL. This was recently extended by Bulwahn (2012a) to also support exhaustive and narrowing-based symbolic testing (Christiansen and Fischer 2008;Lindblad 2007;Runciman et al 2008). Moreover, Bulwahn's tool uses Horn clause data flow analysis to automatically devise generators that only produce data that satisfies the precondition of the tested conjecture (Bulwahn 2012b).…”

Section: Related Workmentioning

confidence: 96%

Testing noninterference, quickly

Hriţcu¹,

Lampropoulos

Spector-Zabusky

et al. 2016

J. Funct. Prog.

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Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

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Section: Related Workmentioning

confidence: 96%

Testing noninterference, quickly

Hriţcu¹,

Lampropoulos

Spector-Zabusky

et al. 2016

J. Funct. Prog.

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show abstract

“…Another important difference is that left-inverse computation is compositional; if we have effective left-inverse computation methods for f and g, we have an effective left-inverse computation method for f • g. On the other hand, right-inverse computation may not be compositional; even if we have right-inverse computation methods for f and g, then right-inverse computation may happen to be undecidable for f • g. Left-inverse computation is suitable for applications in which efficiency is the biggest concern, such as in serialization/deserialization. On the other hand, rightinverse computation is suitable for applications in which one wants to invert non-injective function to enumerate all the corresponding inputs, such as in test-case generation [6,29]. It is worth noting that checking the injectivity of a function is generally undecidable.…”

Section: Related Work 61 Inverse Computationmentioning

confidence: 99%

“…Here, all the picked up (randomly generated or enumerated) data are relevant to the final check in the Step (3). SmallCheck and EasyCheck use inverse computation for efficient testcase generation [6,29], which of course has to be supported by efficient inverse computation.…”

Section: Introductionmentioning

confidence: 99%

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Matsuda

Inaba

Nakano

2012

Higher-Order Symb Comput

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Inverse computation has many applications such as serialization/deserialization, providing support for undo, and test-case generation for software testing. In this paper, we propose an inverse computation method that always terminates for a class of functions known as parameter-linear macro tree transducers, which involve multiple data traversals and the use of accumulations. The key to our method is the observation that a function in the class can be regarded as a non-accumulative context-generating transformation without multiple data traversals. Accordingly, we demonstrate that it is easy to achieve terminating inverse computation for the class by context-wise memoization of the inverse computation results. We also show that when we use a tree automaton to express the inverse computation results, the inverse computation runs in time polynomial to the size of the original output and the textual program size.

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“…GAST is a similar implementation for Clean, while EasyCheck implements random test data generation for Curry (Christiansen and Fischer, 2008). QuickCheck and GAST implement function generators for higher-order function since they deal with higher-order properties while this is not necessary in our approach because such properties are not allowed in Focalize.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, Fisher et al (Fischer and Kuchen, 2007;Fischer and Kuchen, 2008) proposed an original data-flow coverage approach for the testing of Curry programs. This approach is supported by the tool Easycheck (Christiansen and Fischer, 2008). In 2008, FocalTest (Carlier andDubois, 2008), a tool that generates random test data for Focalize programs was proposed.…”

Section: Introductionmentioning

confidence: 99%

Constraint Reasoning in Focaltest

2010

Proceedings of the 5th International Conference on Software and Data Technologies

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Abstract:Property-based testing implies selecting test data satisfying coverage criteria on user-specified properties.However, current automatic test data generation techniques adopt direct generate-and-test approaches for this task. In FocalTest, a testing tool designed to generate test data for programs and properties written in the functionnal language Focal, test data are generated at random and rejected when they do not satisfy selected coverage criteria. In this paper, we improve FocalTest with a test-and-generate approach, through the usage of constraint reasoning. A particular difficulty is the generation of test data satisfying MC/DC on the precondition of a property, when it contains function calls with pattern matching and higher-order functions.Our experimental results show that a non-naive implementation of constraint reasoning on these constructions outperform traditional generation techniques when used to find test data for testing properties.

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EasyCheck — Test Data for Free

Cited by 44 publications

References 14 publications

Testing noninterference, quickly

Testing noninterference, quickly

Polynomial-time inverse computation for accumulative functions with multiple data traversals

Constraint Reasoning in Focaltest

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