A tool for domain-independent model mutation

Gómez‐Abajo, Pablo; Guerra, Esther; Lara, Juan de; Merayo, Mercedes G.

doi:10.1016/j.scico.2018.01.008

Cited by 28 publications

(10 citation statements)

References 14 publications

(32 reference statements)

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“…Another interesting scenario is given when we consider distributed systems, possibly with asynchronous communications, whose specifications are represented as a variant of an FSM [65,66]. In order to confront these more complicated formalisms, we can use current work that make it possible to apply a systematic approach to the generation of mutants [67,68]. In principle, it should also be possible to apply BMI even when there is no specification, since an initial random testing phase could be used to produce estimates of input/output pair frequency.…”

Section: Discussionmentioning

confidence: 99%

Using mutual information to test from Finite State Machines: Test suite selection

Ibias

Núñez

Hierons

2021

Information and Software Technology

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Section: Discussionmentioning

confidence: 99%

Using mutual information to test from Finite State Machines: Test suite selection

Ibias

Núñez

Hierons

2021

Information and Software Technology

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“…As explained in Section 3, our approach relies on mutation analysis to measure the degree of achieved improvement on the test suite quality. Accordingly, we used the WODEL mutant generator [20] to apply the defined mutation operators on the considered models. WODEL generated 394 and 12,087 mutants for the xArduino and the xPSSM models, respectively.…”

Section: Experiments Setupmentioning

confidence: 99%

Automatic test amplification for executable models

Khorram

Bousse

Mottu

et al. 2022

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

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Behavioral models are important assets that must be thoroughly verified early in the design process. This can be achieved with manuallywritten test cases that embed carefully hand-picked domain-specific input data. However, such test cases may not always reach the desired level of quality, such as high coverage or being able to localize faults efficiently. Test amplification is an interesting emergent approach to improve a test suite by automatically generating new test cases out of existing manually-written ones. Yet, while ad-hoc test amplification solutions have been proposed for a few programming languages, no solution currently exists for amplifying the test cases of behavioral models.In this paper, we fill this gap with an automated and generic approach. Given an executable DSL, a conforming behavioral model, and an existing test suite, our approach generates new regression test cases in three steps: (i) generating new test inputs by applying a set of generic modifiers on the existing test inputs; (ii) running the model under test with new inputs and generating assertions from the execution traces; and (iii) selecting the new test cases that increase the mutation score. We provide tool support for the approach atop the Eclipse GEMOC Studio 1 and show its applicability in an empirical study. In the experiment, we applied the approach to 71 test suites written for models conforming to two different DSLs, and for 67 of the 71 cases, it successfully improved the mutation score between 3.17 % and 54.11 % depending on the initial setup. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Model-driven software engineering.

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“…Deriving representatives with mutations. Model mutations can be applied on models to derive models with small differences [25,26,50]. This could be used to automatically derive models similar to selected representative, but the provider of the mutation operators need to ensure that the mutation operators do not hinder the realistic nature or For a sufficiently large set of real models, a representative model can be selected automatically (illustrated in Fig.…”

Section: Manual Selectionmentioning

confidence: 99%

Automated generation of consistent, diverse and structurally realistic graph models

et al. 2021

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In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.

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A tool for domain-independent model mutation

Cited by 28 publications

References 14 publications

Using mutual information to test from Finite State Machines: Test suite selection

Using mutual information to test from Finite State Machines: Test suite selection

Automatic test amplification for executable models

Automated generation of consistent, diverse and structurally realistic graph models

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