Software Quality Assurance Practice Exam

Which black-box methodology focuses on finding a subset of all possible inputs with the highest probability of finding the most errors?

1. Equivalence partitioning

2. Identifying equivalence classes

3. Cause-effect graphing

4. Boundary value analysis

The correct answer is: Equivalence partitioning

Equivalence partitioning is a black-box testing methodology specifically designed to reduce the number of test cases while maintaining adequate coverage of the functionality being tested. It operates on the premise that input data can be divided into classes, where the system's behavior can be expected to be similar for all members of a class. By selecting representative test cases from these equivalence classes, testers can maximize the likelihood of uncovering defects or errors in the software. This approach is particularly efficient because it concentrates testing efforts on a subset of all possible inputs. Rather than checking every single input scenario, which can be infeasible and time-consuming, equivalence partitioning encourages the selection of inputs that represent a wide range of scenarios within those classes. This significantly increases the chances of discovering the most critical errors with fewer test cases. While the other methodologies listed—identifying equivalence classes, cause-effect graphing, and boundary value analysis—are also important for different aspects of testing, they do not specifically focus on minimizing the number of test cases based on the likelihood of error detection as distinctly as equivalence partitioning does. For instance, boundary value analysis focuses specifically on conditions at the edge of input ranges, rather than a broader selection strategy.