Fault domain-based testing in imperfect situations: a heuristic approach and case studies

Belli, Fevzi; Beyazit, Mutlu; Endo, André Takeshi; Mathur, Aditya P.; Simão, Adenilso da Silva

doi:10.1007/s11219-014-9242-6

“…Digital computers can also be seen as systems suitable for this model. Therefore, FSM is quite a common model in computer science and engineering [4][5][6].…”

Section: Testing Finite State Machinesmentioning

confidence: 99%

“…A FSM is a mathematical model with discrete inputs and outputs. It is mostly used for modeling hardware and software systems [4][5][6]. There are several methods proposed in the literature to generate test cases for FSMs.…”

Section: Introductionmentioning

confidence: 99%

Mutant selection by using Fourier expansion

Takan¹,

Ayav²

2020

Turk J Elec Eng & Comp Sci

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Mutation analysis is a widely used technique to evaluate the effectiveness of test cases in both hardware and software testing. The original model is mutated systematically under certain fault assumptions and test cases are checked against the mutants created to see whether the test cases can detect the faults or not. Mutation analysis is usually a computationally intensive task, particularly in finite state machine (FSM) testing due to a possibly huge amount of mutants. Random selection could be a practical reduction method under the assumption that each mutant is identical in terms of the probability of occurrence of its associating fault. The present study proposes a mutant selection method based on Fourier analysis of Boolean functions. Fourier helps to identify the most effective transitions on the output so that the mutants related to those transitions can be selected. Such mutants are considered more important since they are more likely to be killed. To evaluate the method, test cases are generated by the well-known W method, which has the capability of detecting every potential fault. The original and reduced sets of mutants are compared with respect to their importance values. Evaluations show that the mutants selected by the proposed technique are more effective, which reduces the cost of mutation analysis without sacrificing the performance of the mutation analysis.

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“…Digital computers can also be considered as systems that comply with this model. For this reason, FSM is a very widely used model in computer science and engineering (Belli et al, 2015;Fragal et al, 2019;Damasceno, 2016).…”

Section: Finite State Machine Testmentioning

confidence: 99%

Creation of mutants by using centrality criteria in social network analysis

Takan

¹

2020

PeerJ Computer Science

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Mutation testing is a method widely used to evaluate the effectiveness of the test suite in hardware and software tests or to design new software tests. In mutation testing, the original model is systematically mutated using certain error assumptions. Mutation testing is based on well-defined mutation operators that imitate typical programming errors or which form highly successful test suites. The success of test suites is determined by the rate of killing mutants created through mutation operators. Because of the high number of mutants in mutation testing, the calculation cost increases in the testing of finite state machines (FSM). Under the assumption that each mutant is of equal value, random selection can be a practical method of mutant reduction. However, in this study, it was assumed that each mutant did not have an equal value. Starting from this point of view, a new mutant reduction method was proposed by using the centrality criteria in social network analysis. It was assumed that the central regions selected within this frame were the regions from where test cases pass the most. To evaluate the proposed method, besides the feature of detecting all failures related to the model, the widely-used W method was chosen. Random and proposed mutant reduction methods were compared with respect to their success by using test suites. As a result of the evaluations, it was discovered that mutants selected via the proposed reduction technique revealed a higher performance. Furthermore, it was observed that the proposed method reduced the cost of mutation testing.

show abstract

“…Digital computers can also be considered as systems that comply with this model. For this reason, FSM is a very widely used model in computer science and engineering (Belli et al, 2015;Fragal et al, 2018;Damasceno, 2016).…”

Section: Finite State Machine Testmentioning

confidence: 99%

Peer Review #2 of "Creation of mutants by using centrality criteria in social network analysis (v0.1)"

Chen¹

2020

0

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Mutation testing is a method widely used to evaluate the effectiveness of the test suite in hardware and software tests or to design new software tests. In mutation testing, the original model is systematically mutated using certain error assumptions. Mutation testing is based on well-defined mutation operators that imitate typical programming errors or which form highly successful test suites. The success of test suites is determined by the rate of killing mutants created through mutation operators. Because of the high number of mutants in mutation testing, the calculation cost increases in the testing of finite state machines (FSM). Under the assumption that each mutant is of equal value, random selection can be a practical method of mutant reduction. However, in this study, it was assumed that each mutant did not have an equal value. Starting from this point of view, a new mutant reduction method was proposed by using the centrality criteria in social network analysis. It was assumed that the central regions selected within this frame were the regions from where test cases pass the most. To evaluate the proposed method, besides the feature of detecting all failures related to the model, the widely-used W method was chosen. Random and proposed mutant reduction methods were compared with respect to their success by using test suites. As a result of the evaluations, it was discovered that mutants selected via the proposed reduction technique revealed a higher performance. Furthermore, it was observed that the proposed method reduced the cost of mutation testing.

show abstract

Fault domain-based testing in imperfect situations: a heuristic approach and case studies

Cited by 12 publications

References 34 publications

Mutant selection by using Fourier expansion

Mutant selection by using Fourier expansion

Creation of mutants by using centrality criteria in social network analysis

Peer Review #2 of "Creation of mutants by using centrality criteria in social network analysis (v0.1)"

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