Generating software test data by evolution

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

doi:10.1109/32.988709

Cited by 364 publications

(291 citation statements)

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“…Michael and McGraw generated a tool named GADGET to generate test data. The tool makes use of a branch table that keeps a track of all the branch conditions for both of their true and false parts [5].…”

Section: Related Workmentioning

confidence: 99%

“…Reliability based testing criteria are used to show the correctness of the program in terms of achieving some coverage that may be either 'control flow coverage' or 'Data flow coverage'. 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.…”

Section: Introductionmentioning

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%

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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“…Micheal et al [22], Levin and Yehudai [25], Joachim et al [27] indicated that GA outperforms other SBTDG methods e.g. local search or random testing.However eventhough they can generate test data with appropriate fault-prone ability [4,5], they fail to produce them quickly due to their slowly evolutionary speed.…”

Section: Introduction *mentioning

confidence: 99%

“…Even though there have been significant achievements, SE still faces difficulties in handling infinite loops, array, procedure calls and pointer references in each PUT [22].…”

Section: Introduction *mentioning

confidence: 99%

Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Thi

2017

JCSCE

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Search-based test data generation is a very popular domain in the field of automatic test data generation. However, existing search-based test data generators suffer fromsome problems. By combining static program analysis and search-based testing, our proposed approach overcomesone of these problems. Considering the automatic ability and the path coverage as the test adequacycriterion, this paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing. Experimental results demonstrate that our test data generator can generate suitable test data with higher path coverage than the previous one.

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“…Therefore, it is natural for the software engineering community to try to develop methods to reduce the cost associated with testing while enhancing the testing process. Since manual testing is expensive, time consuming and error prone, there has been significant interest in automation [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%