Risk-based testing is tailored to enable organizations to save significant time and costs by targeting their testing efforts to the most critical project areas. It aims to optimize test coverage and quality by focusing on the most critical areas of a project. While risk-based testing is effective, powering it with AI can empower organizations to improve testing efficacy and software quality further while accelerating time to market and slashing costs.
QualityKiosk Technologies put a customized AI-powered risk-based testing model into action to help a top APAC insurer achieve its ambitious digital transformation goal. The client aimed to roll out enhanced digital onboarding experiences in new countries in under 10 weeks without compromising on the project quality and exceeding budgets.
In this case study, you will learn more about how QualityKiosk leveraged AI-based data analytics to help the insurer:
Fill out the form to download the case study to unveil how AI-powered risk-based testing can help you enhance your product and software quality with lean teams and within tight deadlines.
