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Test Data Suit

Exhaustive testing of almost any non-trivial system is impractical due to the fact that the domain of input data values to most practical software system is either extremely large or infinite. Therefore, we must design an optimal test suite that is of reasonable size and can uncover as many errors existing in the system as possible. If test cases are selected randomly, many of them do not contribute to the significance of the test suite , i.e., they do not detect any additional errors not already being detected by other test cases in the suite. Thus, the number of random test cases in a test suite is in general not an indication of the effectiveness of the testing. in other words, testing a system using a large collection of test cases that are selected at random does not guarantee that all of the errors in the system will be uncovered.

Consider the following example code segment for finding the maximum of two integer values x and y. This code segment has a programming error.

If  (x > y) max = x ;
        else max = x ;

For the above code segment, the test set {(X = 3, Y = 2) ; (X = 2, Y = 3)} can detect the error, whereas a larger test set {(x =3, y = 2); (x = 4, y = 3)}; {(x = 5, y = 1)} does not detect the error.

Thus systematic approaches are required to design optimal test sets in which each test case is designed to detect different errors.

There are two main approaches to designing test cases:
•    Black-box approach
•    While-box (or glass-box) approach.

In the black-box approach, test cases are designed using only the functional specification of the software, it. i.e., without any knowledge of the internal structure of the software. For this reason, black-box testing is also known as functional testing.

On the other hand, designing white-box test cases requires through knowledge of the internals structure of software and therefore the while-box testing is also called the structural testing.

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