A taxonomy of model‐based testing approaches

Utting, Mark; Pretschner, Alexander; Legeard, Bruno

doi:10.1002/stvr.456

Cited by 553 publications

(413 citation statements)

References 57 publications

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“…In the BET setting, our optimization shows several advantages. The approach proposed in this paper shares motivation also with research on model-based test generation and combinatorial testing [23,32,37], where constraints are adopted for increasing declarativeness and efficiency.…”

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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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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“…Test inputs are generated by traversing the usage model while respecting transition probabilities in JUMBL: the test cases with the greatest probability are generated first. While using the Markov model to generate testing data, the most common method is one that generates testing data randomly [24][25][26]. However, those approaches do not consider interaction among different operations and the weight of testing data.…”

Section: Model-based Testingmentioning

confidence: 99%

Large scale software test data generation based on collective constraint and weighted combination method

2017

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Original scientific paper Software reliability test is to test software with the purpose of verifying whether the software achieves reliability requirements and evaluating software reliability level. Statistical-based software reliability testing generally includes three parts: building usage model, test data generation and testing. The construction of software usage model should reflect user's real use as far as possible. A huge number of test cases are required to satisfy the probability distribution of the actual usage situation; otherwise, the reliability test will lose its original meaning. In this paper, we first propose a new method of structuring software usage model based on modules and constraint-based heuristic method. Then we propose a method for the testing data generation in consideration of the combination and weight of the input data, which reduces a large number of possible combinations of input variables to a few representative ones and improves the practicability of the testing method. To verify the effectiveness of the method proposed in this paper, four groups of experiments are organized. The goodness of fit index (GFI) shows that the proposed method is closer to the actual software use; we also found that the method proposed in this paper has a better coverage by using Java Pathfinder to analyse the four sets of internal code coverage.Keywords: constraint; data generation; GFI; software reliability testing; usage model; weighted combination Generiranje ispitnih podataka za softver zasnovano na kolektivnom ograničenju i ponderiranoj metodi kombinacijeIzvorni znanstveni članak Ispitivanje pouzdanosti softvera znači ispitivanje softvera kako bi se provjerilo da li udovoljava zahtjevima pouzdanosti i kako bi se procijenio njegov stupanj pouzdanosti. Statistički temeljeno ispitivanje pouzdanosti softvera općenito uključuje tri dijela: izgradnju modela, generiranje ispitnih podataka i ispitivanje. Stvaranje modela upotrebe softvera treba što je više moguće odražavati korisnikovu stvarnu primjenu. Potreban je ogroman broj ispitivanih slučajeva da bi se zadovoljila distribucija vjerojatnoće u slučaju stvarne upotrebe; inače će ispitivanje pouzdanosti izgubiti originalno značenje. U ovom radu najprije predlažemo novu metodu strukturiranja modela primjene softvera zasnovanu na modulima i heurističkoj metodi koja se temelji na ograničenjima. Zatim predlažemo metodu za generiranje podataka za ispitivanje uzimajući u obzir kombinaciju i težinu ulaznih podataka što smanjuje veliki broj mogućih kombinacija ulaznih varijabli na samo nekoliko reprezentativnih i povećava praktičnost primjene ispitne metode. U svrhu provjere učinkovitosti metode predložene u ovom radu, organizirane su četiri grupe eksperimenata. Ispravnost odgovarajućeg indeksa (GFI-goodness of fit index) pokazuje da je predložena metoda bliža upotrebi aktualnog softvera; također smo ustanovili da ima bolju pokrivenost kod uporabe Java Pathfinder-a za analizu četiri niza pokrivenosti internog koda.

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“…If we consider the commonly understood advantages of MBT -such as efficient solutions for test design, increasing the degree of automation, prevention of loss of knowledge by using (semi-)formal models, more systematic and, even most important, repeatability of test case derivation and selfexplanatory of test specifications ( [20], [6] Even though UTP is an expressive language, it does not offer concepts to specify the test design techniques that shall be applied for deriving test artifact. If we consider the before mentioned benefits of MBT, first and foremost test generation, it is most surprising that one of the most important information for automating the test design activities is missing in the test model: The information about which test design techniques shall be carried out on the test design model by the test generator.…”

Section: A Test Design Techniques In Mbtmentioning

confidence: 99%

“…This seems inconsistent, since one of the most communicated benefits of MBT is automated generation of test artifacts and the increased systematics, comprehensibility and repeatability of the test design process [20]. Until today, there is no generally accepted approach found in the literature how test design techniques for model-based test generation shall be specified the best.…”

Section: Introductionmentioning

confidence: 99%