Generating Feasible Test Paths from an Executable Model Using a Multi-objective Approach

Yano, Thaise; Martins, Eliane; Sousa, Fabiano Luís de

doi:10.1109/icstw.2010.52

Cited by 21 publications

(22 citation statements)

References 7 publications

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“…The MOST approach is based on a previous work [8]. The main difference is the model analyzer component that uses dependence analysis, instead of slicing of the model.…”

Section: Related Workmentioning

confidence: 99%

“…As evolutionary algorithm, we use the M-GEO vsl (MultiObjective Generalized Extreme Optimization with variable string length), presented in previous work [8]. As stated before, MOST uses two criteria for test case generation that are represented by two objective functions: the test purpose coverage (F 1 ) and the minimum length of the input sequence (F 2 ).…”

Section: B Multi-objective Evolutionary Approachmentioning

confidence: 99%

“…Early SBST approaches focused mainly on test data generation for codebased testing [5]. Recently, there are initiatives for modelbased testing, in special, from EFSM [6], [7], [8]. A crucial difference in this context is the number of design variables, i.e., elements to be optimized.…”

Section: Introductionmentioning

confidence: 99%

“…To guide the search algorithm, we propose the use of model slicing in a previous work [8], while in this paper we rely solely on dependence analysis. As we are only interested in identifying the parts of the EFSM that affect the test purpose, the dependence analysis is enough for this intent and we do not need to perform all steps to obtain the model slice.…”

Section: Introductionmentioning

confidence: 99%

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MOST: A Multi-objective Search-Based Testing from EFSM

Yano

Martins

Sousa

2011

2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops

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Abstract-This paper introduces a multi-objective evolutionary approach to test case generation from extended finite state machines (EFSM), named MOST. Testing from an (E)FSM generally involves executing various transition paths, until a given coverage criterion (e.g. cover all transitions) is met. As traditional test generation methods from FSM only consider the control aspects, they can produce many infeasible paths when applied to EFSMs, due to conflicts in guard conditions along a path. In order to avoid the infeasible path generation, we propose an approach that obtains feasible paths dynamically, instead of performing static reachability analysis as usual for FSM-based methods. Previous works have treated EFSM test case generation as a mono-objective optimization problem. Our approach takes two objectives into account that are the coverage criterion and the solution length. In this way, it is not necessary to establish in advance the test case size as earlier approaches. MOST constructs a Pareto set approximation, i.e., a group of optimal solutions, which allows the test team to select the solutions that represent a good trade-off between both objectives. The paper shows empirical studies to illustrate the benefits of the approach and comparing the results with the ones obtained in a related work.

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“…The MOST approach is based on a previous work [8]. The main difference is the model analyzer component that uses dependence analysis, instead of slicing of the model.…”

Section: Related Workmentioning

confidence: 99%

Section: B Multi-objective Evolutionary Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MOST: A Multi-objective Search-Based Testing from EFSM

Yano

Martins

Sousa

2011

2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops

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“…For example, chromosome v1 =(0, 1, 2, 3, 4, 12) v2 =(0, 1, 2, 3, 4, 7, 11, 12) fitness value of v1 and v2 is calculated by dividing the chromosome in to adjacent element pairs ((0, 1), (1,2), (2,3), (3,4), (4,12)) and ((0, 1), (1,2), (2,3), (3,4), (4,7), (7, !1), (11, 12)) respectively. The corresponding value of each pair in the adjacent matrix is taken and added together.…”

Section: Test Path Generation Modulementioning

confidence: 99%

Automated Test Path Generation using Genetic Algorithm

K¹,

S²

2017

IJERT

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Abstract-Software testing is the process of finding bugs in the software by executing the program or application. Basis path testing or structured testing is a white box method which analyses the control flow graph to find the set of linear independent path. Path testing is an approach which ensures that each independent path through the program has been executed at least once. Source program is converted to control flow graph and the test path are extracted from this graph. In this paper, we propose an new fitness function for basis test path generation using Genetic algorithm.

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