Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines

Authors

  • Ashok Vootla Senior Software Engineer, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606025

Keywords:

DevSecOps, Pipeline, Accessibility, Testing, AI

Abstract

The present study examines how DevSecOps pipeline accessibility can be ensured through the implementation of automated testing in healthcare systems. The study quantifies the improvements in accessibility at the pre and post automation stages in various web modules using a quantitative design. The results of the axe-core, Pa11y and Lighthouse tools indicate the obvious increase of compliance scores and the decrease of time spent on defects resolution. The results validate the fact that implementing accessibility testing into the pipeline of continuous integration enhances consistency, lessens the amount of manual labor and promotes the adherence to the requirements of WCAG 2.1 over time. The study offers an excellent, data-driven basis on available DevSecOps practices in regulated health settings.

References

[1] Pool, J. R. (2023). Accessibility Metatesting. Accessibility Metatesting, 1–4. https://doi.org/10.1145/3587281.3587282

[2] Hostetler, T. W., Chen, S., Blanco-Cuaresma, S., Accomazzi, A., Kurtz, M. J., Grant, C. S., Henneken, E., Thompson, D. M., Chyla, R., Shapurian, G., Templeton, M. R., Lockhart, K. E., Martinovic, N., McDonald, S., & Grezes, F. (2022). Web accessibility trends and implementation in dynamic web applications. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2202.00777

[3] Brinn, S., Cameron, C., Fielding, D., Frankston, C., Fromme, A., Huang, P., Nazzaro, M., Orphan, S., Sigurdsson, S., Tay, R., Yang, M., & Zhou, Q. (2022). A framework for improving the accessibility of research papers on arXiv.org. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2212.07286

[4] Alsaeedi, A. (2020). Comparing Web Accessibility Evaluation Tools and Evaluating the Accessibility of Webpages: Proposed Frameworks. Information, 11(1), 40. https://doi.org/10.3390/info11010040

[5] Ganesan, J., Azar, A. T., Alsenan, S., Kamal, N. A., Qureshi, B., & Hassanien, A. E. (2022). Deep Learning Reader for Visually Impaired. Electronics, 11(20), 3335. https://doi.org/10.3390/electronics11203335

[6] Martins, B., & Duarte, C. (2022). Large-scale study of web accessibility metrics. Universal Access in the Information Society, 23(1), 411–434. https://doi.org/10.1007/s10209-022-00956-x

[7] Panguraj, A. R. R. (2020). Automated testing of web accessibility: leveraging AI and machine learning for enhanced compliance and user experience. In Journal of Advances in Developmental Research (Vol. 11, Issue 2, pp. 1–2). https://www.ijaidr.com/papers/2020/2/1189.pdf

[8] Chiari, M., Michele, D. P., & Pradella, M. (2022). Static Analysis of Infrastructure as Code: a Survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2206.10344

[9] Nuñez, A., Moquillaza, A., & Paz, F. (2019). Web Accessibility Evaluation Methods: A Systematic Review. Lecture Notes in Computer Science, 226–237. https://doi.org/10.1007/978-3-030-23535-2_17

[10] Doush, I. A., Alkhateeb, F., Maghayreh, E. A., & Al-Betar, M. A. (2012). The design of RIA accessibility evaluation tool. Advances in Engineering Software, 57, 1–7. https://doi.org/10.1016/j.advengsoft.2012.11.004

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Published

2023-12-13

How to Cite

Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. (2023). International Journal of Research and Applied Innovations, 6(6), 9975-9984. https://doi.org/10.15662/IJRAI.2023.0606025