Towards a Framework for Constraint-Based Test Case Generation

Degrave, François; Schrijvers, Tom; Vanhoof, Wim

doi:10.1007/978-3-642-12592-8_10

Cited by 15 publications

(22 citation statements)

References 24 publications

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“…An important aspect of the language is that it allows for a clean modeling of all execution paths, including those that may lead to an erroneous interaction between the program and the database. Secondly, we generalize the white-box test input generation approach from [8] to ImperDB programs. Test inputs can be generated with respect to any (structural) code coverage criterion.…”

Section: Introductionmentioning

confidence: 99%

Test input generation for database programs using relational constraints

Marcozzi

Vanhoof

Hainaut

2012

Proceedings of the Fifth International Workshop on Testing Database Systems

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Databases are ubiquitous in software and testing of programs manipulating databases is thus essential to enhance the reliability of software. In this paper, we describe a clean and unified approach to automatically generate test inputs for such database programs. First, we propose a formal language, called ImperDB, to model database programs. ImperDB allows to model common program behaviors and data structures, as well as typical interaction scenarios between programs and databases. Secondly, we present a static analysis technique to generate test inputs for ImperDB programs, according to any chosen structural adequacy criterion. The technique considers an ImperDB program as a sequence of operations over a set of relational variables, modeling both the database original content and the program inputs. The problem of finding test inputs forcing the execution of a given path can then be transformed into the problem of solving constraints over the relational variables associated to the program. These constraints are expressed with the Alloy language and solved by the Alloy analyzer.

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Section: Introductionmentioning

confidence: 99%

Test input generation for database programs using relational constraints

Marcozzi

Vanhoof

Hainaut

2012

Proceedings of the Fifth International Workshop on Testing Database Systems

Self Cite

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“…Despite theoretical limitations of automatic test case generation 2 , dealing with these hard combinatorial problems for real-world programs is challenging. Constraint-based testing was introduced to tackle these problems for various applications, including automatic white-box test inputs generation for imperative programs [3][4][5][6][7][8][9][10][11][12] and object-oriented programs [13][14][15][16][17][18], test pattern generation in hardware design [19][20][21], model-based test cases generation [22][23][24][25][26][27], and counter-examples generation [28][29][30][31].…”

Section: Constraint-based Testingmentioning

confidence: 99%

Constraint-Based Testing

Gotlieb

2015

Advances in Computers

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“…More recent approaches, such as [11], enable white-box (i.e., code-driven) testing of an imperative language with pointers and heap by symbolic execution of a smallstep operational semantics in CLP. Further work on testing has been done in [8,22] for the generation of heap-allocated data structures, following a fixed coverage criteria for the choice of the test cases.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Efficient generation of test data structures using constraint logic programming and program transformation

Fioravanti

Proietti

Senni

2013

Journal of Logic and Computation

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Abstract. The goal of Bounded-Exhaustive Testing (BET) is the automatic generation of all test cases satisfying a given invariant, within a given size bound. When the test cases have a complex structure, the development of correct and efficient generators becomes a very challenging task. In this paper we use Constraint Logic Programming (CLP) to systematically develop generators of structurally complex test data structures. We follow a declarative approach which allows us to separate the issue of (i) defining the test data structure in terms of its properties, from that of (ii) efficiently generating data structure instances. This separation helps establish the correctness of the developed test case generators. We rely on a symbolic representation and we take advantage of efficient search strategies provided by CLP systems for generating test instances. Through a running example taken from the literature on BET, we illustrate our test generation framework and we show that CLP allows us to develop easily understandable and efficient test generators. Additionally, we propose a program transformation technique whose goal is to make the evaluation of these CLP-based generators much more efficient and we demonstrate its effectiveness on a number of complex test data structures.

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Towards a Framework for Constraint-Based Test Case Generation

Cited by 15 publications

References 24 publications

Test input generation for database programs using relational constraints

Test input generation for database programs using relational constraints

Constraint-Based Testing

Efficient generation of test data structures using constraint logic programming and program transformation

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