Balachandar Ramalingam1, Ashvini Byri2, Ashish Kumar3, Dr Satendra Pal Singh4, Om Goel5
& Prof.(Dr) Punit Goel6
1University of Iowa, Thiruthangal (VIA), Sivakasi – 626130, TamilNadu, India, balachandarramalingam.ai@gmail.com
2University of Southern California, Parel, Mumbai 400012, ashvinieb1@gmail.com
3Tufts University, DMW Colony Patiala, 147003, Punjab India, ashish.93nitj@gmail.com
4Gurukul Kangri University, Haridwar, Uttarakhand , spsingh.gkv@gmail.com
5Abes Engineering College Ghaziabad, omgoeldec2@gmail.com
6Maharaja Agrasen Himalayan Garhwal University, Uttarakhand, drkumarpunitgoel@gmail.com
Abstract
In the rapidly evolving landscape of the insurance industry, large-scale platforms face increasing demands for efficiency, reliability, and regulatory compliance. Automation testing has emerged as a critical solution to address these challenges, enhancing the quality and speed of software delivery. This paper explores advanced techniques in automation testing tailored specifically for large-scale insurance platforms, emphasizing the importance of adopting robust methodologies to ensure seamless functionality across diverse components. Key strategies discussed include the implementation of behavior-driven development (BDD) to foster collaboration between technical and non-technical stakeholders, enabling clearer requirements and improved test coverage. Additionally, leveraging machine learning algorithms for test case generation and prioritization enhances the adaptability of testing processes in dynamic environments. The use of continuous integration and continuous deployment (CI/CD) practices is highlighted as a means to facilitate real-time feedback and accelerate release cycles. Furthermore, the integration of artificial intelligence (AI) in automation testing is examined, showcasing its potential to predict and mitigate risks associated with software updates. The study concludes with recommendations for best practices in automation testing, aimed at optimizing the performance and reliability of large-scale insurance platforms. By adopting these advanced techniques, organizations can significantly improve their testing efficiency, reduce operational costs, and enhance customer satisfaction in an increasingly competitive market.
Keywords:
Advanced automation testing, large-scale insurance platforms, behavior-driven development, machine learning, continuous integration, artificial intelligence, risk mitigation, testing efficiency, software quality assurance, best practices.
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