Evolutionary generation of test data for many paths coverage based on grouping

Gong, Dunwei; Zhang, Wanqiu; Yao, Xiangjuan

doi:10.1016/j.jss.2011.06.028

Cited by 47 publications

(27 citation statements)

References 15 publications

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“…Genetic algorithms (GAs) have been successfully used in software testing activities such as test data generation, (see e.g., [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]). But very little attention has been paid to use GAs in path testing [20].…”

Section: Introductionmentioning

confidence: 99%

“…47.83 % * COVERED DEF-USE PATHS: 1,3,5,6,7,8,10,17,20,21,23 Path 3 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,11,12,13,18,19,20,21,22,23,24,29,30,19,31,32, * DEF-USE COVERAGE:…”

mentioning

confidence: 99%

“…111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, (Path 11) 6. 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, (Path 12) Path 10 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,14,15,16,17,18,19,20,21,25,26,27,28,29,30,19,20 91.30 % * COVERED DEF-USE PATHS: 12 ** hc= 5 ** ** Apply GA to 5 individuals from the current population * Parent 1 (Individual 1): 110111000110101 e0,e1,e3,e4,e10,e12,e9,e5,e14, * Parent 2 (Individual 6): 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, * Parent 3 (Individual 5): 111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, * Parent 4 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 5 (Individual 3): 110111000110101 e0,e1,e3,e4,e10,e12,e9,e5,e14, * Selected Cases to be Parents of New Population are: * Parent 1 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 2 ( Individual 2): 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, * Parent 3 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 4 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 5 (Individual 3): 111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, ** Crossover * Selected Parents: 1, 2…”

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confidence: 99%

“…111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, (Path 17) 6. 110111101111011 e0,e1,e3,e4,e6,e8,e9,e10,e11,e13,e5,e14, (Path 18) Path 18 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,11,12,13,18,19,20,21,22,23,24,29,30,19,20,21,25,26,27,28,29,30,19,31,32, * DEF-USE COVERAGE:…”

mentioning

confidence: 99%

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Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Girgis¹,

Ghiduk²,

Abd-elkawy³

2015

IJCA

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Path testing requires generating all paths through the program to be tested, and finding a set of program inputs that will execute every path. The number of possible paths in programs containing loops is infinite, and so it is very difficult, if not impossible, to test all of them. Path testing can be relaxed by selecting a subset of all executable paths that fulfill a certain path selection criterion and finding test data to cover it. The automatic generation of such test paths leads to more test coverage paths thus resulting in efficient and effective testing strategy. This paper presents a genetical swarm optimization (GSO) based technique, which effectively combines a genetic algorithm (GA) based technique and a particle swarm optimization (PSO) based technique, for automatic generation of a set of test paths that cover the all-uses criterion. Experiments have been carried out to evaluate the effectiveness of the proposed GSO approach in test paths generation compared to the GA and PSO approaches.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Girgis¹,

Ghiduk²,

Abd-elkawy³

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Genetic algorithms (GAs) have been successfully used in software testing activities such as test data generation, (see e.g., [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]). But very little attention has been paid to use GAs in path testing [21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Data Flow Test Paths using a Genetic Algorithm

Girgis¹,

Ghiduk²,

Abd-elkawy³

2014

IJCA

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Path testing a program involves generating all paths through the program, and finding a set of program inputs that will execute every path. Since it is impossible to cover all paths in a program, path testing can be relaxed by selecting a subset of all executable paths that fulfill a certain path selection criterion and finding test data to cover it. The automatic generation of such test paths leads to more test coverage paths thus resulting in efficient and effective testing strategy. This paper presents a structural-oriented technique that uses a genetic algorithm (GA) for automatic generation of a set of test paths that cover the all-uses criterion. In the case of programs that have loops, the proposed technique generates paths according to the ZOT-subset criterion, which requires paths that traverse loops zero, one and two times. The proposed GA uses a binary vector as a chromosome to represent the edges in the DD-graph of the program under test. The set of paths generated by the proposed GA can be passed to a test data generation tool to find program inputs that will execute them. Experiments have been carried out to evaluate the effectiveness of the proposed GA compared to the random test path generation technique. General TermsSoftware Engineering, Software Testing.

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