Genetic algorithms for dynamic test data generation

Michael, C.C.; McGraw, Gary; Schatz, M.; Walton, C.C.

doi:10.1109/ase.1997.632858

Cited by 69 publications

(49 citation statements)

References 4 publications

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“…This study is also an extension to the two existing studies [43] [44], where the performance of random approach and the genetic algorithm are compared. In both of the studies, random approach achieves lower code coverage compared to the genetic algorithm in some cases.…”

Section: The Proposed Approach and Its Implementationmentioning

confidence: 99%

An Efficient Test Data Generation Approach for Unit Testing

Gupta¹,

Khan²

2016

IOSR

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Section: The Proposed Approach and Its Implementationmentioning

confidence: 99%

An Efficient Test Data Generation Approach for Unit Testing

Gupta¹,

Khan²

2016

IOSR

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“…This is facilitated by the fact that many of the software engineering problems can be stated as the search problems. For instance, the testing problem can be stated as the problem of searching the input domain of the program, for those test case values that satisfy the pre defined testing criteria [12].…”

Section: Related Workmentioning

confidence: 99%

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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“…Through instrumentation, the subject program has in effect been converted into another program that computes a function that is to be minimised to zero. This approach has been used by Korel [2], Tracey et al [7], [8], Wegener et al [9], Jones [1] and Michael [4] [3].…”

Section: // Execution Required To Enter This Branchmentioning

confidence: 99%

“…For example, consider the problem of satisfying the condition x = 0 and y = 0. A move by a search algorithm from the (x, y) point (4,6) to the point (3, 5) is rewarded by + in a cost decrease of 2 but max produces only a cost decrease of 1. Similarly, a detrimental move from (4, 6) to (5, 7) is penalised more heavily by + than by max.…”

Section: Analytical Considerationsmentioning

confidence: 99%

Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

Bottaci

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Several researchers are using evolutionary search methods to search for test data with which to test a program. The fitness or cost function depends on the test goal but almost invariably an important component of the cost function is an estimate of the cost of satisfying a predicate expression as might occur in branches, exception conditions, etc. This paper reviews the commonly used cost functions and points out some deficiencies. Alternative cost functions are proposed to overcome these deficiencies. The evidence from an experiment is that they are more reliable.

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Genetic algorithms for dynamic test data generation

Cited by 69 publications

References 4 publications

An Efficient Test Data Generation Approach for Unit Testing

An Efficient Test Data Generation Approach for Unit Testing

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

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