Societal Impact of Test Automation: Reducing Human Error in Critical Systems
DOI:
https://doi.org/10.32628/CSEIT24106184Keywords:
Test Automation, Critical Systems, Error Reduction, Societal Impact, Technological AdvancementAbstract
This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.
Downloads
References
G. Tassey, "The Economic Impacts of Inadequate Infrastructure for Software Testing," National Institute of Standards and Technology, 2002. [Online]. Available: https://www.nist.gov/system/files/documents/director/planning/report02-3.pdf
Capgemini and Micro Focus, "World Quality Report 2021-22," 2021. [Online]. Available: https://www.sogeti.com/explore/reports/world-quality-report-2021-22/
D. F. Sittig et al., "Current challenges in health information technology–related patient safety," Health Informatics Journal, vol. 26, no. 1, pp. 181-189, 2020. [Online]. Available: https://doi.org/10.1177/1460458218814893 DOI: https://doi.org/10.1177/1460458218814893
Deloitte, "Global Risk Management Survey, 12th Edition," 2021. [Online]. Available: https://www2.deloitte.com/content/dam/insights/articles/US103959_Global-risk-management-survey-12ed/DI_Global-risk-management-survey-12ed.pdf
Financial Stability Board, "Artificial intelligence and machine learning in financial services," Nov. 2017. [Online]. Available: https://www.fsb.org/wp-content/uploads/P011117.pdf
E. S. Cochran, M. E. Kohler, D. D. Given, S. Guiwits, J. Andrews, M.-A. Meier, M. Ahmad, I. Henson, R. Hartog, and D. Smith, "Earthquake Early Warning ShakeAlert System: Testing and Certification Platform," Bulletin of the Seismological Society of America, vol. 108, no. 3A, pp. 1665-1682, 2018. [Online]. Available: https://pubs.usgs.gov/publication/70195142 DOI: https://doi.org/10.1785/0220170138
A. Esteva et al., "A guide to deep learning in healthcare," Nature Medicine, vol. 25, no. 1, pp. 24-29, 2019. [Online]. Available: https://doi.org/10.1038/s41591-018-0316-z DOI: https://doi.org/10.1038/s41591-018-0316-z
Federal Reserve Bank of New York, "Quarterly Trends for Consolidated U.S. Banking Organizations," 2021. [Online]. Available: https://www.newyorkfed.org/research/banking_research/quarterly_trends.html
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.