On Random Testing of Image Processing Applications

Mayer, Johannes; Guderlei, Ralph

doi:10.1109/qsic.2006.45

Cited by 37 publications

(32 citation statements)

References 10 publications

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“…Mayer and Guderlei [91], [92] compared several random image generation techniques for testing image processing programs. The study was performed on the implementation of several image operators as the Euclidean distance transform.…”

Section: Computer Graphicsmentioning

confidence: 99%

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A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

432

230

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Abstract-A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output "morphs" into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.

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Section: Computer Graphicsmentioning

confidence: 99%

“…A variant of this metric [71], [91], [121] is often used to calculate the ratio of mutants detected by a given metamorphic relation r as follows:…”

Section: Mutation Scorementioning

confidence: 99%

A Survey on Metamorphic Testing

Segura

Fraser

Sánchez

et al. 2016

IIEEE Trans. Software Eng.

432

230

View full text Add to dashboard Cite

show abstract

“…Synthetic images are also used for testing of image processing applications [23 24]. However, it is difficult [24]. For a binary image, the pixel values are selected with some probability for black and white pixels.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the approach cannot check all the characteristics of the output and only works if statements about the statistical distribution of the test result is possible, which limits the applicability of the approach. In his extended work, Mayer [24] presents assessment of models that can be used for random test data generation and presented a method to generate additional test cases. The validation is performed using mutation analysis for a concrete image processing operation.…”

Section: Discussionmentioning

confidence: 99%

Test Image Generation using Segmental Symbolic Evaluation

Jameel¹,

Lin²

2014

IJNDC

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Image processing applications have played a vital role in modern life and they are required to be well tested due to their significance and human dependence on them. Testing of image processing application is difficult due to complex nature of images in terms of their generation and evaluation. The presented technique is first of its type to generate test images based on symbolic evaluation of program under test. The idea is based on the fact that, neighboring image operations are applied by selecting a segment of image pixels called a window, and iterated by sliding window over entire image. We imitate neighboring operations using symbolic values for the pixels rather than concrete values. The path constraint is extracted for each path in the program under test and solved for concrete solutions. Test images are generated based on solution of path constraints for each identified path. We have tested the proposed scheme on different programs and the results show that test images are successfully generated for each path to ensure the path coverage of the program under test and identifying infeasible paths.

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“…Forrester and Miller (2000) applied RT to test Windows NT applications, and it was observed that 21% of applications were crashed and an additional 24% of applications were hanged. RT has also been used in the testing of communications protocol implementations (West and Tosi, 1995), graphical user interfaces (Dabóczi et al, 2003), Java Just-In-Time compilers (Yoshikawa et al, 2003), embedded software systems (Regehr, 2005), and image processing applications (Mayer and Guderlie, 2006). Moreover, the RT technique has been implemented in many industrial automatic testing tools, such as those developed by IBM (Bird and Munoz, 1983), Microsoft (Slutz, 1998), and Bell Labs (Godefroid et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Adaptive random testing based on distribution metrics

Chen

Kuo

Liu

2009

Journal of Systems and Software

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Random testing (RT) is a fundamental software testing technique.Adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be * A preliminary version of this paper was presented at the 7th International Conference on Quality Software (QSIC 2007) (Chen et al., 2007a

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On Random Testing of Image Processing Applications

Cited by 37 publications

References 10 publications

A Survey on Metamorphic Testing

A Survey on Metamorphic Testing

Test Image Generation using Segmental Symbolic Evaluation

Adaptive random testing based on distribution metrics

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