Test case generation for the task tree type of architecture

Popović, Miroslav; Basicevic, Ilija

doi:10.1016/j.infsof.2010.03.001

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…New Adaptive Random Testing [3,13] which is an alternative method for random testing to improve the failure detection through failure patterns. ART is based on empirical analysis which shows many program faults in failure contiguous areas.…”

Section: Related Workmentioning

confidence: 99%

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Indhumathi¹,

Sarala²

2014

IJCA

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Test case generation is a path to identify the solution in software testing. Adaptive random testing is an enhancement of random testing to improve the quality of fault-revealing. The research focuses on software adaptive random testing based on Matrix called Partitioned Block based Adaptive Random Testing. It compares the performance of PBART with the existing Adaptive random testing using random samples of test cases which are drawn from blocks of distinct partitions. Partition testing defines as a block of test cases partitioned into set of all test cases. Thereby it has prompted to investigate the performance of random testing that can be improved by taking the patterns of failure-causing inputs which utilizes the prior knowledge and the information of the test cases. The proposed algorithm PB -ART performs the testing of program structure and load the source code to matrix with scenarios, method flows and data values. In numerical experiments, the approach examines effectiveness of PB-ART with ordinary adaptive random testing. There exist three measures for evaluating the effectiveness of a testing technique namely P-measure, E-measure and Fmeasure. Moreover F-measure is intuitively more appealing to testers and more realistic and informative from a practical point of view. Therefore, F-measure is chosen for measuring testing techniques in this research work.

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Section: Related Workmentioning

confidence: 99%

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Indhumathi¹,

Sarala²

2014

IJCA

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“…Woit's work has been followed in the area of modelbased testing using the generic modeling environment (Popovic, 2005(Popovic, , 2006a(Popovic, , b, 2007. More recently, Woit's approach has been adapted for testing large-scale task tress (Popovic, 2010). Of course, many other researchers have been active in the area of SUT.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, there is a need to adapt the existing methods for this new kind of parallel programs, and maybe explore new approaches and invent new methods. Recently, Woit's approach to statistical usage testing (SUT) and reliability estimation (Woit, 1993a(Woit, , b, 1994aBroakman, 2002) has been adapted for testing large-scale task trees based on operational profiles (Popovic, 2010). In this paper, we present two novel methods for testing task trees, which we developed by adapting the traditional methods for testing sequential programs.…”

Section: Introductionmentioning

confidence: 99%

Generic method for statistical testing of parallel programs based on task trees

Popović¹,

Kupresanin²,

Basicevic³

2012

Sci. Res. Essays

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This paper deals with a particular class of parallel programs, which are based on task trees. The main objective of this paper was to adapt the generic method for statistical testing of sequential programs (GMST-SP) for this class of parallel programs, such that adapted method (GMST) can treat a family of task trees rather than just a single task tree, and that it can respect various evolutions of individual task trees. In this paper, we compare GMST with the adapted exhaustive testing method (ET) and with the previously adapted statistical usage testing method (SUT), based on experimentally measured testing effort and path coverage. GMST and SUT both have better deep path coverage than ET. SUT requires less testing effort than GMST and ET, but its deep path coverage decreases with the number of tasks. Finally, GMST has advantage over SUT because it provides constant mean level of deep path coverage, which can be regulated by the required testing quality.

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Diversity driven adaptive test generation for concurrent data structures

Chen

2018

Information and Software Technology

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Test case generation for the task tree type of architecture

Cited by 8 publications

References 24 publications

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Generic method for statistical testing of parallel programs based on task trees

Diversity driven adaptive test generation for concurrent data structures

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