VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems

Name: VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems
Author: Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan

İsim	VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems
Yazar	Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan
Basım Tarihi:	2018-02
Basım Yeri	- The ACM Digital Library
Konu	Black-box testing, Test oracle, Computer vision, Image processing, Test automation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0164-1212
Kayıt Numarası	08925302-f268-4e4d-883d-935bf84d823f
Lokasyon	Computer Science
Tarih	2018-02
Örnek Metin	A test oracle automation approach proposed for systems that produce visual output.Root causes of accuracy issues analyzed for test oracles based on image comparison.Image processing techniques employed to improve the accuracy of test oracles.A fast image processing pipeline developed as an automated test oracle.An industrial case study performed for automated regression testing of Digital TVs. Test oracles differentiate between the correct and incorrect system behavior. Hence, test oracle automation is essential to achieve overall test automation. Otherwise, testers have to manually check the system behavior for all test cases. A common test oracle automation approach for testing systems with visual output is based on exact matching between a snapshot of the observed output and a previously taken reference image. However, images can be subject to scaling and translation variations. These variations lead to a high number of false positives, where an error is reported due to a mismatch between the compared images although an error does not exist. To address this problem, we introduce an automated test oracle, named VISOR, that employs a fast image processing pipeline. This pipeline includes a series of image filters that align the compared images and remove noise to eliminate differences caused by scaling and translation. We evaluated our approach in the context of an industrial case study for regression testing of Digital TVs. Results show that VISOR can avoid 90% of false positive cases after training the system for 4h. Following this one-time training, VISOR can compare thousands of image pairs within seconds on a laptop computer.
DOI	10.1016/j.jss.2017.06.023
Cilt	136

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VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems

Yazar Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan

Basım Tarihi 2018-02

Basım Yeri - The ACM Digital Library

Konu Black-box testing, Test oracle, Computer vision, Image processing, Test automation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0164-1212

Kayıt Numarası 08925302-f268-4e4d-883d-935bf84d823f

Lokasyon Computer Science

Tarih 2018-02

Örnek Metin A test oracle automation approach proposed for systems that produce visual output.Root causes of accuracy issues analyzed for test oracles based on image comparison.Image processing techniques employed to improve the accuracy of test oracles.A fast image processing pipeline developed as an automated test oracle.An industrial case study performed for automated regression testing of Digital TVs. Test oracles differentiate between the correct and incorrect system behavior. Hence, test oracle automation is essential to achieve overall test automation. Otherwise, testers have to manually check the system behavior for all test cases. A common test oracle automation approach for testing systems with visual output is based on exact matching between a snapshot of the observed output and a previously taken reference image. However, images can be subject to scaling and translation variations. These variations lead to a high number of false positives, where an error is reported due to a mismatch between the compared images although an error does not exist. To address this problem, we introduce an automated test oracle, named VISOR, that employs a fast image processing pipeline. This pipeline includes a series of image filters that align the compared images and remove noise to eliminate differences caused by scaling and translation. We evaluated our approach in the context of an industrial case study for regression testing of Digital TVs. Results show that VISOR can avoid 90% of false positive cases after training the system for 4h. Following this one-time training, VISOR can compare thousands of image pairs within seconds on a laptop computer.

DOI 10.1016/j.jss.2017.06.023

Cilt 136