Metamorphic Testing of Image Region Growth Programs in Image Processing Applications

Jiang, Chao; Huang, Song; Hui, Zhanwei

doi:10.1109/qrs-c.2018.00026

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…In [49], the experiment verifies the effectiveness and correctness of the proposed technique in alleviating the Oracle problem in a region growth program. The testing method successfully detects all the embedded mutants.…”

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confidence: 79%

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Cao

et al. 2020

Applied Sciences

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Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, such as functional error, failure and low performance. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: (1) the quality attributes that are focused for the quality of big data applications, including correctness, performance, availability, scalability and reliability, and the factors influencing them; (2) the existing implementation-specific QA technologies, including specification, architectural choice and fault tolerance, and the process-specific QA technologies, including analysis, verification, testing, monitoring and fault and failure prediction; (3) existing strengths and limitations of each kind of QA technology; (4) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications and can help developers of big data applications apply suitable QA technologies.

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mentioning

confidence: 79%

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Cao

et al. 2020

Applied Sciences

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“…It is concluded that for bug identification, specific images are needed instead of general input images such as Lena. Jiang et al [19] applied MT to the image region growth program. Mutation testing is used to find the effectiveness of MRs.…”

Section: Related Workmentioning

confidence: 99%

“…Afterward, the same test cases, t 1 and t ′ 1 , can be run on the mutated program p ′ with mutant m. Record the outputs as r 2 and r ′ 2 . If both (r 1 , r ′ 1 ) and (r 2 , r ′ 2 ) satisfy their related MR, then the mutant m is not killed [19]. Otherwise, the mutant is killed.…”

Section: Mr Evaluation Using Mutation Testingmentioning

confidence: 99%

“…The mutation score indicates the fault detection rate of each MR. In the existing literature, i.e., [12,14,16,19], all the MRs are evaluated by seeding faults manually in the code or through a tool that calculates the mutation score automatically through the traditional mutation testing approach. We have seeded the faults manually and checked the relation manually on both the programs (original and mutated) for the calculation of the mutation score.…”

Section: Mr Evaluation Using Mutation Testingmentioning

confidence: 99%

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Measuring Effectiveness of Metamorphic Relations for Image Processing Using Mutation Testing

Jafari,

Nadeem

2024

J. Imaging

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Testing an intricate plexus of advanced software system architecture is quite challenging due to the absence of test oracle. Metamorphic testing is a popular technique to alleviate the test oracle problem. The effectiveness of metamorphic testing is dependent on metamorphic relations (MRs). MRs represent the essential properties of the system under test and are evaluated by their fault detection rates. The existing techniques for the evaluation of MRs are not comprehensive, as very few mutation operators are used to generate very few mutants. In this research, we have proposed six new MRs for dilation and erosion operations. The fault detection rate of six newly proposed MRs is determined using mutation testing. We have used eight applicable mutation operators and determined their effectiveness. By using these applicable operators, we have ensured that all the possible numbers of mutants are generated, which shows that all the faults in the system under test are fully identified. Results of the evaluation of four MRs for edge detection show an improvement in all the respective MRs, especially in MR1 and MR4, with a fault detection rate of 76.54% and 69.13%, respectively, which is 32% and 24% higher than the existing technique. The fault detection rate of MR2 and MR3 is also improved by 1%. Similarly, results of dilation and erosion show that out of 8 MRs, the fault detection rates of four MRs are higher than the existing technique. In the proposed technique, MR1 is improved by 39%, MR4 is improved by 0.5%, MR6 is improved by 17%, and MR8 is improved by 29%. We have also compared the results of our proposed MRs with the existing MRs of dilation and erosion operations. Results show that the proposed MRs complement the existing MRs effectively as the new MRs can find those faults that are not identified by the existing MRs.

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“…All mobile apps are started from an idea. This idea leads to the development of the application [45].…”

Section: Q Inceptionmentioning

confidence: 99%

Quality assurance for mobile applications

Basit¹,

Khan²,

Nisa³

2020

J. adv. tec. eng. res

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Metamorphic Testing of Image Region Growth Programs in Image Processing Applications

Cited by 6 publications

References 4 publications

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Measuring Effectiveness of Metamorphic Relations for Image Processing Using Mutation Testing

Quality assurance for mobile applications

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