Search-Based Test Data Generator for Data-Flow Dependencies Using Dominance Concepts, Branch Distance and Elitism

Varshney, Sapna; Mehrotra, Monica

doi:10.1007/s13369-015-1921-5

Cited by 24 publications

(14 citation statements)

References 26 publications

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“…Therefore, the integer test inputs are converted in binary using (8× number of inputs) bits for reproduction (8 bits are sufficient to represent each test input in the range [−10, 110]). There are variants for chromosomes encoding in GA e.g., gray, binary and, real, and each has its own advantages and disadvantages [68][69][70]. Binary encoding is beneficial to include a sudden change in the population of solutions, which is desirable in the current study to diversify the population for increasing the chances of detecting live mutants.…”

Section: Description Of Proposed Approachmentioning

confidence: 99%

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

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Section: Description Of Proposed Approachmentioning

confidence: 99%

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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“…As per the study claim, this approach achieved 100 % path and boundary coverage. Varshney and Mehrotra [7] suggested GA based approach for generating optimized test cases for data flow testing while considering all uses criterion as testing adequacy criterion. This paper also introduces a fitness function by using dominance concept in control flow graph, elitism and branch distance.…”

Section: A Ga In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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The intention behind software testing is discovering defects in the developed software, making it error-free, robust and trustworthy. A large quantum of total efforts in the development of software is done on software testing. These efforts may include time, cost and most importantly manpower. The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software. In literature, various nature inspired algorithms are used for the optimization of the software testing process. This article is a brief study on the applications of nature inspired algorithms in software testing. To keep the study succinct this study considers only two widely used testing types, structural and functional testing and literature available, since 2010 by considering only SCI or SCOPUS indexed publications.

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“…The distance of the branch is usually within the limit [0,1]. Modified branch distance bd(x,tc), where x corresponds the target node and tc denotes the test case in the current population is calculated using equation 5 [39].…”

Section: B Branch Distancementioning

confidence: 99%

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

2019

IJRTE

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Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

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Search-Based Test Data Generator for Data-Flow Dependencies Using Dominance Concepts, Branch Distance and Elitism

Cited by 24 publications

References 26 publications

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Research on use of Nature Inspired Algorithms in Software Testing

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

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