Black box testing treats the system as a “black-box”, so it doesn’t explicitly use Knowledge of the internal structure or code. Or in other words the Test engineer need not know the internal working of the “Black box” or application.
Main focus in black box testing is on functionality of the system as a whole. The term ‘behavioral testing’ is also used for black box testing and white box testing is also sometimes called ’structural testing’. Behavioral test design is slightly different from black-box test design because the use of internal knowledge isn’t strictly forbidden, but it’s still discouraged.
Each testing method has its own advantages and disadvantages. There are some bugs that cannot be found using only black box or only white box. Majority of the applications are tested by black box testing method. We need to cover majority of test cases so that most of the bugs will get discovered by blackbox testing.
Black box testing occurs throughout the software development and testing life cycle i.e. in Unit, Integration, System, Acceptance and regression testing stages.
Tools used for Black Box testing:
Black box testing tools are mainly record and playback tools. These tools are used for regression testing that to check whether new build has created any bug in previous working application functionality. These record and playback tools records test cases in the form of some scripts like TSL, VB script, Java script, Perl.
Advantages of Black Box Testing:
- Tester can be non-technical.
- Used to verify contradictions in actual system and the specifications.
- Test cases can be designed as soon as the functional specifications are complete
Disadvantages of Black Box Testing:
- The test inputs needs to be from large sample space.
- It is difficult to identify all possible inputs in limited testing time. So writing test cases is slow and difficult
- Chances of having unidentified paths during this testing
Methods of Black box Testing:
Graph Based Testing Methods:
Each and every application is build up of some objects. All such objects are identified and graph is prepared. From this object graph each object relationship is identified and test cases written accordingly to discover the errors.
Error Guessing:
This is purely based on previous experience and judgment of tester. Error Guessing is the art of guessing where errors can be hidden. For this technique there are no specific tools, writing the test cases that cover all the application paths.
Equivalence Partitioning:
Equivalence partitioning is a black box testing method that divides the input domain of a program into classes of data from which test cases can be derived.
How this partitioning is performed while testing:
1. If an input condition specifies a range, one valid and one two invalid classes are defined.
2. If an input condition requires a specific value, one valid and two invalid equivalence classes are defined.
3. If an input condition specifies a member of a set, one valid and one invalid equivalence class is defined.
4. If an input condition is Boolean, one valid and one invalid class is defined.
In this technique, we do not use the code to determine a test suite; rather, knowing the problem that we're trying to solve, we come up with four types of test data:
1. Easy-to-compute data
2. Typical data
3. Boundary / extreme data
4. Bogus data
For example, suppose we are testing a function that uses the quadratic formula to determine the two roots of a second-degree polynomial ax2+bx+c. For simplicity, assume that we are going to work only with real numbers, and print an error message if it turns out that the two roots are complex numbers (numbers involving the square root of a negative number).
We can come up with test data for each of the four cases, based on values of the polynomial's discriminant (b2-4ac):
Easy data ( discriminant is a perfect square):
a b c Roots
1 2 1 -1, -1
1 3 2 -1, -2
Typical data (discriminant is positive):
a b c Roots
1 4 1 -3.73205, -0.267949
2 4 1 -1.70711, -0.292893
Boundary / extreme data (discriminant is zero):
a b c Roots
2 -4 2 1, 1
2 -8 8 2, 2
Bogus data ( discriminant is negative, or is zero):
a b c Roots
1 1 1 square root of negative number
0 1 1 division by zero
As with glass-box testing, you should test your code with each set of test data. If the answers match, then your code passes the black-box test.
Boundary Value Analysis:
Many systems have tendency to fail on boundary. So testing boundary values of application is important. Boundary Value Analysis (BVA) is a test Functional Testing technique where the extreme boundary values are chosen. Boundary values include maximum, minimum, just inside/outside boundaries, typical values, and error values.
BVA techniques:
1. Number of variables
For n variables: BVA yields 4n + 1 test cases .
2. Kinds of ranges
Generalizing ranges depends on the nature or type of variables
Advantages of Boundary Value Analysis
1. Robustness Testing – Boundary Value Analysis plus values that go beyond the limits
2. Min – 1, Min, Min +1, Nom, Max -1, Max, Max +1
3. Forces attention to exception handling
Limitations of Boundary Value Analysis
Boundary value testing is efficient only for variables of fixed values in boundary.
EXAMPLE:
For the function that computes the square root of integer values in the range 0 and 5000, the test case must include the following values: {0, -1, 5000, 5001}
