Euclide: A Constraint-Based Testing Framework for Critical C Programs

Gotlieb, Arnaud

doi:10.1109/icst.2009.10

Cited by 32 publications

(30 citation statements)

References 35 publications

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“…These seminal works yield the development of GATEL, a tool that generates test cases for reactive programs written in Lustre. In 1998, Gotlieb et al proposed using constraint techniques to generate test data for C programs (Gotlieb et al, 1998). This approach was implemented in tools InKa and Euclide (Gotlieb, 2009).…”

Section: Related Workmentioning

confidence: 99%

“…The extraction of constraints and their resolution have required to adapt the techniques developed in (Gotlieb et al, 1998) to the specification and implementation language of Focalize, which is a functional one, close to ML. In particular, an important technical contribution of the paper concerns the introduction in CLP(FD) of constraints related to values of concrete types, i.e.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constraint Reasoning in Focaltest

2010

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

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“…Constraint Based Testing (CBT) and Search Based Testing (SBT) have become the dominant approaches to test case generation, because they achieve high code coverage. Over the last decade, theses two approaches have been extensively explored [3,5,9,10,14,18,19].…”

Section: Introductionmentioning

confidence: 99%

“…We distinguish between two static CBT approaches: path-oriented [2] and goal-oriented [5]. The first finds test inputs of a given execution path.…”

Section: Introductionmentioning

confidence: 99%

Boosting Search Based Testing by Using Constraint Based Testing

Sakti

Guéhéneuc

Pesant

2012

Lecture Notes in Computer Science

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Abstract. Search-Based Testing (SBT) uses an evolutionary algorithm to generate test cases. Traditionally, a random selection is used to generate an initial population and also, less often, during the evolution process. Such selection is likely to achieve lower coverage than a guided selection. We define two novel concepts: (1) a constrained population generator (CPG) that generates a diversified initial population that satisfies some test target constraints; and (2) a constrained evolution operator (CEO) that evolves test candidates according to some constraints of the test target. Either the CPG or CEO may substantially increase the chance of reaching adequate coverage with less effort. In this paper, we propose an approach that models a relaxed version of the unit under test as a constraint satisfaction problem. Based on this model and the test target, a CPG generates an initial population. Then, an evolutionary algorithm uses a CEO and this population to generate test input leading to the test target being covered. Our approach combines constraint-based testing (CBT) and SBT and overcomes the limitations associated with each of them. Using eToc, an open-source SBT tool, we implement a prototype of this approach. We present the empirical results of applying both CPG or CEO on three open-source programs and show that CPG or CEO improve SBT performance in terms of branch coverage by 11% while reducing computation time.

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“…BINTEST [10] developed by Beydeda et al guided the search procedure with bisection, which could eliminate the domains of variables that possibly included some solutions. Euclide proposed by Arnaud Gotlieb [11] was a testing tool based on constraint solving, and was employed to verify safety-critical C programs, combining symbolic and numerical analyses, constraint propagation, integer linear relaxation and search-based test data generation. Pachauri et al [12] introduced three methods to order branches for selecting targets for coverage testing, and conducted experiments to evaluate branch ordering through memory and elitism to increase the performance of test data generation.…”

Section: Introductionmentioning

confidence: 99%

A hybrid backtracking algorithm for automatic test data generation

et al. 2017

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Original scientific paper As a fundamental issue in software testing, automatic test data generation is of crucial importance, which is essentially a constraint satisfaction problem and solved by search algorithms. In our previous research, branch and bound was proposed as our constraint solver and the look-ahead methods were elaborated. Based on interval arithmetic and symbolic execution, this paper focuses on the look-back or backtracking method, which is the hybridization of forward checking and conflict-directed backjumping, with the aim of improving the efficiency of backtracking in the search procedure. The closures of variables are used to facilitate the localization of the conflicts which cause dead ends. Empirical experiments prove the effectiveness of the proposed hybrid backtracking method and its applicability in engineering. Keywords: automatic test data generation; backtracking; closure; conflict-directed backjumping; forward checking Hibridni unatražni algoritam za automatsko generiranje podataka za ispitivanjeIzvorni znanstveni članak Kao osnovno pitanje u ispitivanju softvera, automatsko generiranje podataka za ispitivanje je od najveće važnosti, što je u biti problem zadovoljavanja ograničenja, a rješava se algoritmima pretraživanja. U našem prethodnom istraživanju za rješenje ograničenja predložena je metoda grananja i ograničavanja, a elaborirane su unaprijedne metode. Zasnovan na intervalnom aritmetičkom i simboličkom izvršenju, ovaj je rad usredotočen na unatražnu metodu, a to je povezivanje unaprijednog provjeravanja i unatražnog ispitivanja usmjerenog konflikta, u cilju poboljšanja učinkovitosti unatražnog praćenja u postupku traženja. Zatvaranja varijabli se koriste kako bi se olakšalo lociranje nesuglasica koje dovode do neriješenih rezultata (dead ends). Empirijski eksperimenti dokazuju učinkovitost predložene hibridne unatražne metode i njenu primjenljivost u inženjerstvu.Ključne riječi: automatsko generiranje podataka za ispitivanje; unaprijedno provjeravanje; unatražno praćenje; unatražno skakanje na osnovu konflikta; zatvaranje

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Euclide: A Constraint-Based Testing Framework for Critical C Programs

Cited by 32 publications

References 35 publications

Constraint Reasoning in Focaltest

Constraint Reasoning in Focaltest

Boosting Search Based Testing by Using Constraint Based Testing

A hybrid backtracking algorithm for automatic test data generation

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