Screenshot testing as a multi-aspect type of automated dynamic verification for web applications
- Authors: Makarov K.S.1, Fatkin R.I.1
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Affiliations:
- Issue: No 1 (2025)
- Pages: 32-54
- Section: Articles
- URL: https://journals.rcsi.science/2454-0714/article/view/359386
- DOI: https://doi.org/10.7256/2454-0714.2025.1.73535
- EDN: https://elibrary.ru/UVGEBC
- ID: 359386
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Abstract
The subject of this study is multi-aspect screenshot testing as a modern method of automated dynamic verification of web applications, combining functional testing and user interface (UI) validation. Contemporary testing methods face challenges such as high labor intensity, false positives, and low scalability, especially in complex projects. The main objective of the research is to develop and implement a method that improves defect detection accuracy, reduces testing time, and lowers test case development costs. The study explores image comparison algorithms, dynamic element filtering techniques, and automated UI analysis approaches to enhance efficiency and standardization in the web application verification process. Unlike functional and UI testing conducted separately, the proposed method enables simultaneous analysis of multiple aspects of the interface and functionality, minimizing labor costs and increasing testing reliability. The approach employs automated comparison of reference and test screenshots at the pixel, structural element, and content levels using Python, Selenium, PIL, and Pytest-xdist for parallel test execution, effectively addressing the challenges of web application verification. Some researchers in the field of testing agree that the testing process lacks standardization and clear evaluation criteria. The proposed method ensures the achievement of verification objectives even under evolving strategies and approaches to system performance assessment by creating a flexible and precise validation system that integrates various testing types into a unified structure, making it suitable for modern software development challenges. The experimental section demonstrates the advantages of multi-aspect screenshot testing over other methods, including reduced testing time, improved defect detection accuracy, and enhanced analysis of test reports. This approach can be adapted to various testing scenarios and is particularly beneficial for high-load projects requiring regular regression testing.
About the authors
Konstantin Sergeevich Makarov
Email: makarov_ks@kursksu.ru
Ruslan Igorevich Fatkin
Email: ruslan4631@yandex.ru
ORCID iD: 0009-0006-3481-6122
References
The Economic Impacts of Inadequate Infrastructure for Software Testing. NIST Report, May 2002. Режим доступа: https://www.nist.gov/system/files/documents/director/planning/report02-3.pdf (Дата обращения: 05.10.2024). Гурин Р. Е., Рудаков И. В., Ребриков А. В. Методы верификации программного обеспечения // Машиностроение и компьютерные технологии. 2015. № 10. С. 235-251. Quadri S. M. K., Farooq S. U. Software testing-goals, principles, and limitations // International Journal of Computer Applications. 2010. Т. 6. № 9. С. 7-9. Kumar S. Reviewing software testing models and optimization techniques: an analysis of efficiency and advancement needs // Journal of Computers, Mechanical and Management. 2023. Т. 2. № 1. С. 43-55. Xie Q., Memon A. M. Designing and comparing automated test oracles for GUI-based software applications // ACM Transactions on Software Engineering and Methodology (TOSEM). 2007. Т. 16. № 1. С. 4. Кудрявцева Е. Ю. Автоматизированное тестирование веб-интерфейсов // Горный информационно-аналитический бюллетень (научно-технический журнал). 2014. № S. С. 354-356. Кулямин В. В. Методы верификации программного обеспечения / В. В. Кулямин. М.: ИСП РАН, 2008. 111 с. Персиваль Г. Python. Разработка на основе тестирования / Г. Персиваль. М.: ДМК Пресс, 2018. 622 с. Берегейко О. П., Дубовский А. С. Автоматизация тестирования веб-приложений // Вестник магистратуры. 2016. № 12-4 (63). С. 39-41. Dwarakanath A., Neville D., Sanjay P. Machines that test Software like Humans. arXiv preprint arXiv:1809.09455 (2018).
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