Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage

Ghiduk, Ahmed S.; Harrold, Mary Jean; Girgis, Moheb R.

doi:10.1109/apsec.2007.100

Cited by 29 publications

(19 citation statements)

References 15 publications

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“…Girgis proposed an approach to generate test data using 'du (definition use) paths coverage testing criteria'. In their work, they focused upon generating the dominance tree from the control flow graph of the program [8].…”

Section: Related Workmentioning

confidence: 99%

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An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra¹,

Garg²

2011

Journal of Information Processing Systems

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Abstract-As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

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Section: Related Workmentioning

confidence: 99%

“…Control flow coverage criteria include path testing [4], condition testing [5], branch testing [6,7], etc. Data flow coverage criteria include all-uses [8], all-du (definition use) paths [9,23,26,27] etc. Reliability based criteria aims to generate a set of reliable test cases.…”

Section: Introductionmentioning

confidence: 99%

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra¹,

Garg²

2011

Journal of Information Processing Systems

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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:…”

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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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