CurryCheck: Checking Properties of Curry Programs

Hanus, Michael

doi:10.1007/978-3-319-63139-4_13

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…Other directly related systems are EasyCheck [2] and CurryCheck [9] for the Curry language. In these systems test cases are generated from the (strong) types present in the language, as in QuickCheck.…”

Section: : -mentioning

confidence: 99%

An Integrated Approach to Assertion-Based Random Testing in Prolog

Casso

Morales

López-García

et al. 2020

Lecture Notes in Computer Science

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We present an approach for assertion-based random testing of Prolog programs that is tightly integrated within an overall assertionbased program development scheme. Our starting point is the Ciao model, a framework that unifies unit testing and run-time verification, as well as static verification and static debugging, using a common assertion language. Properties which cannot be verified statically are checked dynamically. In this context, the idea of generating random test values from assertion preconditions emerges naturally since these preconditions are conjunctions of literals, and the corresponding predicates can in principle be used as generators. Our tool generates valid inputs from the properties that appear in the assertions shared with other parts of the model, and the run time-check instrumentation of the Ciao framework is used to perform a wide variety of checks. This integration also facilitates the combination with static analysis. The generation process is based on running standard predicates under non-standard (random) search rules. Generation can be fully automatic but can also be guided or defined specifically by the user. We propose methods for supporting (C)LP-specific properties, including combinations of shape-based (regular) types and variable sharing and instantiation, and we also provide some ideas for shrinking for these properties. We also provide a case study applying the tool to the verification and checking of the code of some of the abstract domains used by the Ciao system. Research partially funded by MINECO TIN2015-67522-C3-1-R TRACES project, and the Madrid P2018/TCS-4339 BLOQUES-CM program. We are also grateful to the anonymous reviewers for their useful comments. At run time, sorted(Ys) will be called within the assertion checking-harness, right after calls to qs/2. This harness ensures that the variable bindings (or constraints) and the whole checking process are kept isolated from the normal execution of the program (this can be seen conceptually as including a Prolog copy_term, or calling within a double negation, \+\+, executing in a separate process, etc.). Testing vs. Run-Time Checking: The checking of sorted/1 in the example above will occur in principle during execution of the program, i.e., in deployment. However, in many cases it is not desirable to wait until then to detect errors. This is the case for example if errors can be catastrophic or perhaps if there is interest in testing, perhaps for debugging purposes, more general properties that have not been formally proved and whose main statements are not directly part of the program (and thus, will never be executed), such as, e.g.:

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Section: : -mentioning

confidence: 99%

An Integrated Approach to Assertion-Based Random Testing in Prolog

Casso

Morales

López-García

et al. 2020

Lecture Notes in Computer Science

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“…However, CurryCheck generates only total values for input parameters of properties. Instead of defining specific generators for partial values (note that Cur-ryCheck also supports the definition of user-defined generators for input parameters, as described in [21]), we exploit the already available partial data type P -AB. This type contains an explicit representation of an undefined element but, due to typing reasons, P -AB values cannot be used as inputs to f and g. However, we can map these values into the desired AB values: Such instances are generated for all partial data types involved in properties.…”

Section: Generating Partial Valuesmentioning

confidence: 99%

“…This motivates the use of property testing tools which automate the checking of properties by random or systematic generation of test inputs. Property-based testing has been introduced with the QuickCheck tool [12] for the functional language Haskell and adapted to other languages, like PrologCheck [1] for Prolog, PropEr [33] for the concurrent functional language Erlang, and Easy-Check [10] and CurryCheck [21] for the functional logic language Curry. If the test data is generated in a systematic (and not random) manner, like in SmallCheck [37], GAST [27], EasyCheck [10], or CurryCheck [21], these tools can actually verify properties for finite input domains.…”

Section: Property-based Checkingmentioning

confidence: 99%

“…Property-based testing has been introduced with the QuickCheck tool [12] for the functional language Haskell and adapted to other languages, like PrologCheck [1] for Prolog, PropEr [33] for the concurrent functional language Erlang, and Easy-Check [10] and CurryCheck [21] for the functional logic language Curry. If the test data is generated in a systematic (and not random) manner, like in SmallCheck [37], GAST [27], EasyCheck [10], or CurryCheck [21], these tools can actually verify properties for finite input domains. In the following, we show how to extend the property-based test tool CurryCheck to support equivalence checking of operations.…”

Section: Property-based Checkingmentioning

confidence: 99%

“…From these results, we show how propertybased testing can be used for this purpose. Based on these results, we extend an existing property-based test tool for functional logic programs [21] to test the equivalence of operations. This is the basis of a software package manager with semantic versioning checking [22].…”

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Equivalence Checking of Non-deterministic Operations

Antoy

Hanus²

2018

Functional and Logic Programming

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Checking the semantic equivalence of operations is an important task in software development. For instance, regression testing is a routine task performed when software systems are developed and improved, and software package managers require the equivalence of operations in different versions of a package within the same major number version. A solid foundation is required to support a good automation of this process. It has been shown that the notion of equivalence is not obvious when non-deterministic features are present. In this paper, we discuss a general notion of equivalence in functional logic programs and develop a practical method to check it. Our method is integrated in a property-based testing tool which is used in a software package manager to check the semantic versioning of software packages. MotivationFunctional logic languages combine the most important features of functional and logic programming in a single language (see [4,20] for recent surveys). In this paper we consider Curry [24], a contemporary functional logic language that conceptually extends Haskell with common features of logic programming. Hence, a Curry function is evaluated lazily and may be non-deterministic when defined by overlapping rules. As discussed in [7], the combination of these features raises new issues for defining the equivalence of expressions. Actually, three different notions of equivalence can be distinguished:1. Ground equivalence: Two expressions are equivalent if they produce the same results when their variables are replaced by ground terms. 2. Computed-result equivalence: Two expressions are equivalent if they produce the same outcomes, i.e., variables in expressions are considered as free variables which might be instantiated during the evaluation process. 3. Contextual equivalence: Two expressions are equivalent if they produce the same outcomes in all possible contexts.⋆ This is an extended version of a paper appeared in → denotes the reflexive and transitive closure of this reduction relation. The equivalence of this rewrite relation and CRWL is shown in [28]. Equivalent OperationsAs discussed above, equivalence of operations can be defined in different ways. Ground equivalence and computed result equivalence only compare the values of applications. This is too weak since some operations have no finite values.Example 3. Consider the following operations that generate infinite lists of numbers: ints1 n = n : ints1 (n+1) ints2 n = n : ints2 (n+2)Lemma 2. Let x be an integer and rs and xs lists of integers such that x ≤ z for all z ∈ rs. Then, for some list of integers r, minRest x rs xs * → (m, r) with m ≤ z for all z ∈ (x : rs++xs) and perm (x : rs++xs) * → m : r (where rs++xs denotes the concatenation of the lists rs and xs).Proof. We assume that x ≤ z for all z ∈ rs. We prove the lemma by induction on the length of the list xs.Base case: xs = []:Since minRest x rs [] *

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Concolic Testing of Functional Logic Programs

Tikovsky¹

2018

Declarative Programming and Knowledge Management

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CurryCheck: Checking Properties of Curry Programs

Cited by 9 publications

References 36 publications

An Integrated Approach to Assertion-Based Random Testing in Prolog

An Integrated Approach to Assertion-Based Random Testing in Prolog

Equivalence Checking of Non-deterministic Operations

Concolic Testing of Functional Logic Programs

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