Many quality teams measure success by how many test scripts are written and deployed and how that stacks up against the defined test coverage. These are the pragmatic metrics we choose given the all too frequent tight time constraints and the constantly evolving application look, feel, and behavior.

But now that AI is providing the opportunity for teams to achieve massively greater efficiency through autonomous testing, these measures fall short. Application coverage is a more complete measure of the effectiveness of your testing program, yielding more issues more quickly.

In this webinar, we’ll explain how application coverage is calculated and show how, with AI, you can achieve unheard levels of application coverage without having to complete every test case. We’ll explain how application coverage differs from test coverage, and what you can expect by adopting this measure of success.

Join us to learn:

- How application coverage is defined and measured
- How application coverage compares to test coverage
- The implications of achieving high levels of application coverage in your quality program

Join us to learn how to benefit from adopting a testing approach that maximizes application coverage, achieved with AI-driven autonomous testing!
Kevin Parker
Vice President of Customer Success, Appvance
Kevin is a 30-year industry veteran and the holder of four technology patents. He specializes in the Digital Transformation of Business Systems through DevOps, Agile, Shift Left, software development, software testing, software release management, digital transformation, micro-services architectures, and web-services architectures. In addition to working with Appvance customers to ensure their success, he has been crossing the globe working closely with analysts, the press, customers, partners, and employees to exchange ideas about industry direction and business issues.
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