Efficient Combinatorial Test Generation Based on Multivalued Decision Diagrams

Gargantini, Angelo; Vavassori, P.

doi:10.1007/978-3-319-13338-6_17

Cited by 14 publications

(10 citation statements)

References 23 publications

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“…MEDICI stays in between ACTS and CASA. MEDICI_1_1_1 produces test suites a little smaller then ACTS's ones but it is 10 times slower, MEDICI_10_30_5 produces test suites comparable to CASA but it can be faster [8]. Overall, ACTS is chosen in the 85% of cases for a single test cost of 0.01s and its percentage continuously decreases until 18% at the test cost of 500s while the percentages of the other generators increase.…”

Section: Methodsmentioning

confidence: 95%

“…MEDICI, as many other tools, can be fine tuned by using options: we use a fast variant (MEDICCLLl) and a more slow one (MEDICClO_30_5) which should produce fewer tests [8]. We use the same tool with two different configurations in order to prove that our approach could be used to decide the parameters for a single given tool.…”

Section: B Data Understanding and Preparationmentioning

confidence: 98%

“…CITLAB has been designed tightly integrated with the Eclipse IDE framework, by means of its plug-in extension mechanism. CITLAB already supports three main generation techniques: ACTS [4], [5], CASA [6], [7], and MEDICI [8]. ACTS is a tool developed by the NIST and implements several variants of the In Parameter Order (IPO) strategy.…”

Section: B Citlabmentioning

confidence: 99%

“…Namely, the algorithms for CIT test generations supported in CITLAB are ACTS [4], [5], CASA [6], [7], and MEDICI [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using decision trees to aid algorithm selection in combinatorial interaction tests generation

Gargantini

Vavassori

2015

2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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It has been widely observed that there is no a single best CIT generation algorithm; instead, different algorithms perform best in terms of test suite size and time, also depending on different combinatorial models. Rather than following the traditional approach of leaving the choice of the best generator for a given class of models and for given testing requirements to the user, we want to automate the algorithm selection process among a given set of techniques (called portfolio). The proposed approach takes as input a distribution of combinatorial models and their test suites generated using several tools, then, using data-mining techniques, it permits to predict the algorithm that performs better given the cost estimated to execute a single test and the model characteristics. As predictors, we decide to use decision trees because they have been one of the most widely used decision support tool for many years. Their attraction lies in the simplicity of the resulting model, where a decision tree (at least one that is not too large) is quite easy to view, understand, and, importantly, explain even if it may not always deliver the best performances. We demonstrate the effectiveness of our approach to automated algorithm selection in extensive experimental results on data sets including models commonly presented in literature.

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Section: Methodsmentioning

confidence: 95%

Section: B Data Understanding and Preparationmentioning

confidence: 98%

Section: B Citlabmentioning

confidence: 99%

“…Namely, the algorithms for CIT test generations supported in CITLAB are ACTS [4], [5], CASA [6], [7], and MEDICI [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using decision trees to aid algorithm selection in combinatorial interaction tests generation

Gargantini

Vavassori

2015

2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

Self Cite

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show abstract

“…In the experiments, we will be able to compute the models afi by assuming that we know the exact model of the system. We use multi-valued decision diagrams (MDDs) [12] in order to calculate the number of tests that are permitted by the constraints in the model.…”

Section: Smentioning

confidence: 99%

Combinatorial Interaction Testing for Automated Constraint Repair

Gargantini

Petke

Radavelli

2017

2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Highly-configurable software systems can be easily adapted to address user's needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system's configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system's parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models.

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A novel combinatorial testing approach with fuzzing strategy

Chen

Cai

et al. 2023

J Software Evolu Process

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SummaryCombinatorial testing (CT) is considered as a practical approach to detect software faults, which has arisen from the interaction between factors affecting the software behavior. However, most of the traditional algorithms on CT generation did not take advantage of the execution results of the earlier test cases, as well as neglect the impact of the nonequilibrium input parameter model (NE‐IPM) effect on redundant test cases, which bring a deleterious effect to the detection accuracy of the software faults. To solve these problems, we propose a novel CT approach with fuzzing strategy called CTAF. Based on the idea that fuzzing is performed during execution, CTAF exploits the execution results of earlier tests to provide guidance for subsequent test generation thereby reducing the redundant test cases without compromising the diversity of test cases. And then, we designed three experiments on real subjects of six open source software systems, and the experimental results show that the proposed CTAF approach can effectively improve the NE‐IPM effect and enhance the detection accuracy of software faults.

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Efficient Combinatorial Test Generation Based on Multivalued Decision Diagrams

Cited by 14 publications

References 23 publications

Using decision trees to aid algorithm selection in combinatorial interaction tests generation

Using decision trees to aid algorithm selection in combinatorial interaction tests generation

Combinatorial Interaction Testing for Automated Constraint Repair

A novel combinatorial testing approach with fuzzing strategy

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