All Pair Testing in SDLC
In this article, I am going to discuss All Pair Testing in SDLC. Please read our previous article where we discussed Decision Table in Testing. At the end of this article, you will understand the following important pointers which are related to All Pair Testing in SDLC.
- What is All Pair Testing?
- What is the Goal of All-Pair Testing?
- Real-World Example to Understand All-Pair Testing
- When should we use a Pairwise Test?
- What tools are used for All-Pair Testing?
- What are the Advantages of All-Pair Testing?
- What are the Disadvantages of All-Pair Testing?
What is All Pair Testing?
Pairwise testing is another name for the all-pairs testing technique. It is used to evaluate all conceivable discrete value combinations. This combinational method is used to test applications that include checkbox input, radio button input (used when there is only one choice to choose from, such as when selecting gender as male or female), list box, text box, etc. A software application’s output is influenced by a variety of variables, including input parameters, state variables, and environment setups. To determine the potential values for specific elements, methods such as boundary value analysis and equivalence partitioning can be helpful. However, it would be impracticable to test every potential set of values for each of those variables. In order to satisfy all factors, a subset of combinations is created instead.
What is the Goal of All-Pair Testing?
All pair testing’s main goal is to look at all potential pairs of test results and determine whether the application behaves as intended.
- To produce a smaller set of input/output values in order to effectively cover the test cases.
- To design test cases more quickly and efficiently so we can detect bugs.
- To develop a perfect data set for the automation toolkit.
- To deliver goods more quickly and at a high standard.
Real-World Example to Understand All-Pair Testing:
Pairwise testing, commonly referred to as all-pairs testing, is a black box testing technique that aids in lowering the necessary test case count while maintaining enough coverage. It works especially well when handling a variety of input combinations. The method makes sure that every pair of input values is covered in the test cases at least once.
Username: Can be “admin”, “user”, or “guest”
Password: Can be “password123”, “qwerty”, or “abc123”
Language: Can be “English”, “Spanish”, or “French”
To apply the all-pairs testing technique, we need to generate test cases that cover all possible pairs of values. The following table represents the combination of input values for the test cases:
Nine test cases are provided in this example, covering all pairs of potential combinations. Only one instance of each pair of input values happens throughout all test cases, ensuring thorough coverage. We have greatly lowered the number of test cases needed while still testing different input value combinations by employing all-pairs testing. This method aids in maximizing test coverage and reducing the amount of time and effort needed for testing.
When should we use a Pairwise Test?
Experimental data from a work on combinatorial testing indicates that roughly 60–95% of problems are caused by the interplay of two parameters. If we test 2-way combinations using Pairwise testing, we can identify a significant portion of mistakes. We can use all-pair testing:
- When there are numerous parameters, such as numerous configurations and various input variables.
- It can be utilized when the application is in a critical state and we are unable to do thorough testing because of particular reasons.
- Whether automating the application is possible.
The test analyst or test lead must decide whether pairwise testing should be utilized to test their product, even though it can be quite effective at finding problems or faults in the product. The ‘coupling effect’, which was the subject of study by Jefferson Offutt in 1992, is the foundation for pairwise testing. According to this discovery, if a product is specifically tested for minor errors, significant sophisticated faults are also examined implicitly. The setting of a “smaller subset of configuration variables” is therefore likely to be the cause of a fault if it arises as a result of settings applied to specific configuration variables.
What tools are used for All-Pair Testing?
Tools that use the all-pairs testing technique are available, enabling us to efficiently automate the Test Case Design process by creating the required Test Cases from a small number of parameter value options. The following are a few popular industrial tools:
- PICT – Microsoft Corporation’s ‘Pairwise Independent Combinatorial Testing’ (PICT) service.
- IBM FoCuS – IBM’s “Functional Coverage Unified Solution” is known as FoCuS.
- ACTS – ACTS, or “Advanced Combinatorial Testing System,” is a service offered by NIST, a US government organization.
- Pairwise by Inductive AS
- VPTag free All-Pair Testing Tool
What are the Advantages of All-Pair Testing?
All-pair testing, also known as pairwise testing, offers several advantages:
- Effective Test Coverage: All-pair testing delivers great coverage with few test cases by choosing pairs of input parameters for testing. It guarantees that every feasible set of parameter combinations is evaluated at least once.
- Takes Less Time and Effort: All-pair testing takes less time and effort to complete than exhaustive testing because there are fewer test cases to run. The overall testing workload is decreased while allowing for complete coverage.
- Finding Errors: All-pair testing is efficient at finding errors brought on by interactions between input parameters. It raises the likelihood of finding errors or bugs early in the testing process by covering a wide range of combinations.
- Cost-Effective: All-pair testing can assist minimize costs related to test preparation, execution, and maintenance because there are fewer test cases. It keeps a high degree of test coverage while optimizing resource usage.
- Faster Feedback Loop: All-pair testing’s effectiveness makes it possible to receive input on the system being tested more quickly. Teams can iterate and address problems right away by quickly identifying faults, which leads to shorter development cycles and faster time to market.
What are the Disadvantages of All-Pair Testing?
There are several drawbacks to this strategy despite the fact that it has many advantages. These are the main ideas:
- Limited Coverage: When there are a lot of input parameters, all-pair testing might not completely cover all conceivable combinations. It’s possible that some combinations won’t be tested, which could result in faults being missed.
- Implementation Complexity: Developing and maintaining an all-pair test suite might be difficult. It takes rigorous organization and coordination to create every pairwise combination conceivable, especially in intricate systems with several input variables.
- Increased Test Maintenance: The entire test suite may need to be changed or produced when changes are made to the system or the input parameters. This can mean that maintaining tests will take more time and effort.
- All-pair testing efficiently detects interactions between two parameters, but it may miss higher-order interactions involving three or more parameters. This restriction might cause problems that result from intricate interactions to go unnoticed.
- Prioritizing tests is difficult since all-pair testing generates a lot of test cases, making it difficult to rank them in order of importance. This may lead to ineffective test execution and the potential oversight of critical flaws.
In the next article, I am going to discuss Cause Effect Graph Testing. Here, in this article, I try to explain All Pair Testing in SDLC. I hope you enjoy this All-Pair Testing article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.