Can quality of Quality Assurance be assured?

In a typical software development project, Quality Assurance (QA) process ensures that the quality of the code, written by developers, is compliant with the requirements defined by business users. The IT QA team, as part of their test design, makes sure that all requirements are covered by the test cases, and thus all possible defects can be identified and fixed before the application is exposed to the business users for User Acceptance Testing (UAT).

But who measures the quality of Quality Assurance? And, how?

Let me share what I experienced so far in my career. At the end of a project, QA is considered to be well-performed if no (or may be a very few cosmetic) defects are percolated to the upper environments (like UAT and/or production). But this is a very rare event. In real-life projects, with almost certainty, some moderately critical (if not very critical) defects are always uncovered by the User Acceptance testers (in UAT environment) or business users (in production environment). And, when they are uncovered, the IT QA team had already missed the bus!

But, wouldn't it be helpful if the quality of test cases could be measured before even executing them in lower environments? This way the IT QA team would have had a chance to make corrections and adjustments to their test cases, instead of waiting for the UAT team to discover their mistakes!

Some organizations thought about this problem and came up with an solution where IT QA team reviews the test cases with Business Analysts to make sure all test conditions are covered. But the big problem with this approach is, still the quality of test cases is not measurable. And, because it is not measurable, it is not optimized. So, the set of test cases often grow exponentially in order to ensure that all possible combination of requirements are covered.

Testing Algorithms proposes a solution by which the quality of test cases can be measured and optimized, even before the test execution begins. This solution ensures the coverage of maximum requirement combinations by minimum number of test cases. And, that too, with at least 90% less test design effort.