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Combination testing strategies: A survey
 Software Testing, Verification, and Reliability
, 2005
"... Combination strategies are test case selection methods that identify test cases by combining values of the different test object input parameters based on some combinatorial strategy. This survey presents 16 different combination strategies, covering more than 40 papers that focus on one or several ..."
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Combination strategies are test case selection methods that identify test cases by combining values of the different test object input parameters based on some combinatorial strategy. This survey presents 16 different combination strategies, covering more than 40 papers that focus on one or several combination strategies. This collection represents most of the existing work performed on combination strategies. This survey describes the basic algorithms used by the combination strategies. Some properties of combination strategies, including coverage criteria and theoretical bounds on the size of test suites, are also included in this description. This survey paper also includes a subsumption hierarchy that attempts to relate the various coverage criteria associated with the identified combination strategies.
Input parameter modeling for combination strategies
, 2007
"... Combination strategies are test methods that generate test cases based on input parameter models. This paper suggests a structured modeling method used to translate requirements expressed in a general format into an input parameter model suitable for combination strategies. This paper also describes ..."
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Combination strategies are test methods that generate test cases based on input parameter models. This paper suggests a structured modeling method used to translate requirements expressed in a general format into an input parameter model suitable for combination strategies. This paper also describes results from two initial experiments exploring the efficiency and effectiveness of the modeling method. These results indicate that the resulting models may contain enough information to detect the vast majority of faults in the system under test. Further, results indicate that the modeling method is simple enough to use in practical testing.
Managing conflicts when using combination strategies to test software
 In Proceedings of 18th Australian Conference on Software Engineering (ASWEC2007
, 2007
"... Testers often represent systems under test in input parameter models. These contain parameters with associated values. Combinations of parameter values, with one value for each parameter, are potential test cases. In most models, some values of two or more parameters cannot be combined. Testers must ..."
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Testers often represent systems under test in input parameter models. These contain parameters with associated values. Combinations of parameter values, with one value for each parameter, are potential test cases. In most models, some values of two or more parameters cannot be combined. Testers must then detect and avoid or remove these conflicts. This paper proposes two new methods for automatically handling such conflicts and compares these with two existing methods, based on the sizes of the final conflictfree test suites. A test suite reduction method, usable with three of the four investigated methods is also included in the study, resulting in seven studied conflict handling methods. In the experiment, the number and types of conflicts, as well as the size of the input parameter model and the coverage criterion used, are varied. All in all, 3854 test suites with a total of 929, 158 test cases were generated. Two methods stand out as tractable and complementary. The best method with respect to test suite size is to avoid selection of test cases with conflicts. However, this method cannot always be used. The second best method, removing conflicts from the final test suite, is completely general. 1.
Using an existing suite of test objects: Experience from a testing experiment
 ACM SIGSOFT Software Engineering Notes
, 2004
"... This workshop paper presents lessons learned from a recent experiment to compare several test strategies. The test strategies were compared in terms of the number of tests needed to satisfy them and in terms of faults found. The experimental design and conduct are discussed, and frank assessments o ..."
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This workshop paper presents lessons learned from a recent experiment to compare several test strategies. The test strategies were compared in terms of the number of tests needed to satisfy them and in terms of faults found. The experimental design and conduct are discussed, and frank assessments of the decisions that were made are provided. The paper closes with a summary of the lessons that were learned.
A Prioritized Test Generation Method for Pairwise Testing
, 2012
"... The most sufficient test methods are based on a test case set which covers all combinations of parameters. However, the scale of test cases is always too large and their cost cannot be accepted. People will first consider the implementation of critical test cases. Even if the test is terminated sudd ..."
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The most sufficient test methods are based on a test case set which covers all combinations of parameters. However, the scale of test cases is always too large and their cost cannot be accepted. People will first consider the implementation of critical test cases. Even if the test is terminated suddenly, the test cases of high importance will have been executed. It improves the testing efficiency while securing the detection rate of defect. The contribution of this paper is how to generate pairwise testing cases with a priority. Firstly, We design formulas to compute the weights of priorities. Secondly, we adopt a greed algorithm to solve the combined testing problems. Furthermore, we integrate the greed strategy into a genetic algorithm which makes the most efficient testing in critical parameters and their sets, and ensures its detection rate of defect under limited resources.
Generating Combinatorial Test Suite with Solution Space Tree for Configurations Testing of Sensors Networks
"... Abstract: There are many results about generating pairwise covering arrays with strength τ=2 have been reported, but fewer results are published for highstrength covering arrays with a higherstrength τ>2. In configuration testing of sensor networks, highstrength covering array is required to ..."
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Abstract: There are many results about generating pairwise covering arrays with strength τ=2 have been reported, but fewer results are published for highstrength covering arrays with a higherstrength τ>2. In configuration testing of sensor networks, highstrength covering array is required to construct combinatorial test cases. To generate combinatorial test suite with higherstrength, a backtracking algorithms, which is based on solution space tree, is proposed in this paper by extending an existing pairwise combinatorial test suite generation algorithm. In solution space tree model, each test case is represented as a path from the root to a leaf node in the tree. And proposed algorithm generates test cases one by one, by backtracking depthfirst searching in the solution space tree. Finally, to assess the efficiency of proposed algorithm, computational comparison with other published methods is reported.
Six Issues in Testing EventTriggered RealTime Systems
"... Verification of realtime systems is a complex task, with problems coming from issues like concurrency. A previous paper suggested dealing with these problems by using a timetriggered design, which gives good support both for testing and formal analysis. However, a timetriggered solution is not al ..."
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Verification of realtime systems is a complex task, with problems coming from issues like concurrency. A previous paper suggested dealing with these problems by using a timetriggered design, which gives good support both for testing and formal analysis. However, a timetriggered solution is not always feasible and an eventtriggered design is needed. Eventtriggered systems are far more difficult to test than timetriggered systems. This paper revisits previously identified testing problems from a new perspective and identifies additional problems for eventtriggered systems. The paper also presents an approach to deal with these problems. The TETReS project assumes a modeldriven development process. We combine research within three different fields: (i) transformation of rule sets between timed automata specifications and ECA rules with maintained semantics, (ii) increasing testability in eventtriggered system, and (iii) development of test case generation methods for eventtriggered systems.
Research Article Adaptive Random Testing with Combinatorial Input Domain
"... Copyright © 2014 Rubing Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Random testing (RT) is a fundamental testing tec ..."
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Copyright © 2014 Rubing Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Random testing (RT) is a fundamental testing technique to assess software reliability, by simply selecting test cases in a random manner from the whole input domain. As an enhancement of RT, adaptive random testing (ART) has better failure−detection capability and has been widely applied in different scenarios, such as numerical programs, some object−oriented programs, and mobile applications. However, notmuchwork has been done on the effectiveness of ART for the programswith combinatorial input domain (i.e., the set of categorical data). To extend the ideas to the testing for combinatorial input domain, we have adopted different similarity measures that are widely used for categorical data in data mining and have proposed two similarity measures based on interaction coverage. Then, we propose a new version named ART−CID as an extension of ART in combinatorial input domain, which selects an element from categorical data as the next test case such that it has the lowest similarity against already generated test cases. Experimental results show that ART−CID generally performs better than RT, with respect to different evaluationmetrics. 1.
Empirically Identifying the Best Genetic Algorithm for Covering Array Generation
"... With their many interacting parameters, modern software systems are highly configurable. Combinatorial testing is a widely used and practical technique that can detect the failures triggered by the parameters and their interactions. One of the key challenges in combinatorial testing is covering arra ..."
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With their many interacting parameters, modern software systems are highly configurable. Combinatorial testing is a widely used and practical technique that can detect the failures triggered by the parameters and their interactions. One of the key challenges in combinatorial testing is covering array generation,