More may not always be merrier. Surely in the case of Quality Assurance. With a zeal to ensure that all the test scenarios are covered, QA teams tend to create a large number of test cases. Ensuring the adequate test coverage is not an easy task as it takes considerable time to create test cases and data ensuring that all the required combination of parameters is included in the test cases and all the required scenarios are covered. It may only burden the scarce resources further and may increase the testing cycle time. The challenge is in finding the right mix of combinations and optimum number of test cases that will provide adequate coverage.
The question is “What is adequate coverage?”. Answer to this question is subjective and highly tilted towards the experience and judgement of the QA team. Normally, Quality Assurance teams decide the quantum of the test cases basis their previous experience of testing, business knowledge and confidence level.
While it is not possible to entirely remove the subjectivity of the QA team, it is possible to help the QA team to reduce their burden. Our flagship solution @TEST can help the teams in deciding the optimum amount of test coverage while not compromising the quality of testing. Its robust algorithms generate Test Cases and Test Data automatically for both positive and negative Test Scenarios. @TEST has the capability to build the robust combinations of various data values for all types of data whether it is numeric range, list of values either picked from a table of values or entered on adhoc basis. @TEST can ensure adequate coverage across the numeric range picking the data randomly from the predefined segments and in multiples of step as required. Backed by the strength of @TEST, QA team can be rest assured about the parameter coverage and combinations.
As the load of ensuring the adquacy of the combinations takenover by @TEST, QA team can concentrate on planning their test cycle and other priorities where their judgement is necessary. There are many methods to decide the optimum number of cases for test coverage. The decision on optimum number of cases to be tested depends on various factors like impact on end customer, priority of the feature or scenario and the available time and resources. @TEST can be helpful in these areas too.
By deciding the adequate number of test cases that can provide optimum test coverage, QA team can maximise their delivery capabilities.