Cause effect graph in software testing is a technique used in software testing to help identify the possible causes of defects or errors in a software system. It involves mapping the input variables (causes) to the output variables (effects) of a software system and creating a graphical representation of their relationships.
Cause Effect Graph in Software Testing
The cause effect graph can be used to identify the inputs that are likely to cause defects and to design test cases that cover all possible combinations of inputs. This technique can help to reduce the number of test cases required to achieve high test coverage and to identify defects early in the development process.
The process of cause effect graphing involves the following steps:
- Identify the input variables of the software system that may affect its behavior or output.
- Identify the output variables of the software system that may be affected by the input variables.
- Create a table or matrix that maps the input variables to the output variables, and indicates the possible values for each input variable.
- Draw a cause-effect graph that shows the relationships between the input and output variables.
- Identify the test conditions or combinations of input values that will be covered by each test case.
- Use the cause-effect graph to design test cases that cover all possible combinations of input values.
Cause Effect Graph in Software Testing Example
Let’s consider an example of a simple login system that takes input from the user in the form of a username and a password. The system checks the validity of the credentials and then allows access to the system.
In this case, the input variables are the username and the password, and the output variables are access granted or access denied. Let’s create a cause effect graph for this system:
- Username: valid, invalid
- Password: valid, invalid
- Access granted: yes, no
In this graph, we have mapped the input variables to the output variables, and we can see that certain combinations of input variables lead to specific output variables. For example, if the username is valid and the password is valid, access is granted. On the other hand, if the username is invalid, access is denied regardless of the password entered.
Using this cause-effect graph, we can design test cases that cover all possible combinations of input values and verify that the system behaves as expected. For example, we can design test cases to cover scenarios such as:
- A valid username and valid password (access granted)
- A valid username and invalid password (access denied)
- Invalid username and valid password (access denied)
- Invalid username and invalid password (access denied)
Why Would A Tester Use A Cause Effect Graph?
A tester may use a cause-effect graph to design effective test cases that cover all possible combinations of input values and verify the behavior of the system under different conditions. The technique is especially useful when dealing with complex systems with many input variables and outputs. Some of the key benefits of using a cause-effect graph in software testing are:
- Comprehensive test coverage: Cause-effect graphing ensures that all possible combinations of input variables are tested, which helps to identify defects and ensure comprehensive test coverage.
- Efficiency: By identifying the relationships between input variables and outputs, cause-effect graphs allow testers to design test cases that are more efficient and effective in identifying defects.
- Clarity: Cause-effect graphs provide a clear visual representation of the system, which makes it easier to understand how the input variables affect the output variables.
- Simplification: Cause-effect graphs can simplify complex systems by breaking them down into smaller components that can be more easily tested.
- Requirement Validation: By linking the requirements to the cause effect graph, it can help in validating the requirements by ensuring that all requirements are covered in the test cases.
Cause Effect Graph Notation
Cause effect graphing uses a specific notation to represent the relationships between input variables and output variables. The notation includes the following symbols:
- Input Variables: Input variables are represented as rectangles and are labeled with the name of the variable. For example, if we are testing a software program that calculates the area of a rectangle, the input variables could be the length and width of the rectangle, represented as “Length” and “Width” in rectangles.
- Output Variables: Output variables are represented as ovals and are labeled with the name of the variable. For example, if the program outputs the area of the rectangle, the output variable could be “Area” represented as an oval.
- Arrows: Arrows are used to show the relationship between the input and output variables. Each arrow represents a possible cause-and-effect relationship between two variables. For example, if the area of the rectangle is the product of the length and the width, an arrow would be drawn from “Length” and “Width” to “Area.”
- Conditions: Conditions are represented as diamonds and are used to show constraints on the input variables. For example, if the length and width must be positive integers, a condition would be drawn as a diamond with the label “Positive Integers” and arrows would originate from the condition to both input variables.
- Labels: Labels are used to indicate the value ranges for the input variables or output variables. For example, if the length and width must be between 1 and 10, labels could be added to the “Length” and “Width” rectangles to show their allowable ranges.