Automatic Test Image Generation using Procedural Noise
IEEE/ACM International Conference on Automated Software Engineering
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Patrick, M., Castle, M., Stutt, R., & Gilligan, C. (2016). Automatic Test Image Generation using Procedural Noise. IEEE/ACM International Conference on Automated Software Engineering https://doi.org/10.17863/CAM.4579
It is difficult to test programs that input images, due to the large number of (pixel) values that must be chosen and the complex ways these values interact. Typically, such programs are tested manually, using images that have known results. However, this is a laborious process and limited in the range of tests that can be applied. We introduce a new approach for testing programs that input images automatically, using procedural noise and spatial statistics to create inputs that are both realistic and can easily be tuned to have specific properties. The effectiveness of our approach is illustrated on an epidemiological simulation of a recently introduced tree pest in Great Britain: Oriental Chestnut Gall Wasp. Our approach produces images that match the real landscapes more closely than other techniques and can be used (alongside metamorphic relations) to detect smaller (artificially introduced) errors with greater accuracy.
software testing, image processing, test data generation
This work was supported by the University of Cambridge/Wellcome Trust Junior Interdisciplinary Fellowship “Making scientific software easier to understand, test and communicate through modern advances in software engineering.
This record's DOI: https://doi.org/10.17863/CAM.4579
This record's URL: https://www.repository.cam.ac.uk/handle/1810/260348