How to Perform Orthogonal Array Testing?

# How to Perform Orthogonal Array Testing?

Updated on Dec 21, 2023 17:33 IST

Software testing is a vital component in the development process, ensuring that applications are robust and reliable. Among various testing methodologies, Orthogonal Array Testing (OAT) stands out for its efficiency and effectiveness. This article explores the nuances of OAT, shedding light on its importance, techniques, tools, and the challenges encountered during its implementation.

## What is Orthogonal Array Testing (OAT)?

Orthogonal Array Testing (OAT) is a systematic and statistical approach to software testing. It is used to reduce the number of test cases while maintaining test coverage. OAT is based on orthogonal arrays, which are mathematical tables used to identify variations in test data.

This method is particularly effective for systems with multiple inputs and complex configurations, as it helps in identifying the optimal combination of test cases to cover most scenarios.

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## Why Conduct Orthogonal Array Testing?

Orthogonal Array Testing is conducted due to its:

• Efficiency in Test Coverage: Ensures broad coverage with a minimal set of test cases.
• Time-Saving Capabilities: Reduces the time needed for test case design and execution.
• Effectiveness in Complex Systems: Ideal for systems with numerous variables and interactions.
• Reduction in Redundancy: Minimizes repetitive tests, focusing only on necessary combinations.

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## Which Techniques are Used for Orthogonal Array Testing?

Orthogonal Array Testing (OAT) employs a range of sophisticated techniques to optimize test case selection and ensure comprehensive coverage:

• Selection of Orthogonal Arrays: The foundational step in OAT is selecting the right orthogonal array. This involves determining the number of tests required based on the number of parameters and their levels. The chosen array dictates the combination of parameters for each test case, ensuring a balanced representation of all possible scenarios.
• Parameter Definition and Level Assignment: Each parameter involved in the test is defined, and levels are assigned based on the possible values or states each parameter can have. This step is crucial for accurately mapping the test scenario onto the orthogonal array.
• Pairwise Testing: Often used in conjunction with OAT, pairwise testing ensures that every possible pair of parameter values is tested at least once. This technique is particularly effective in identifying interaction defects between pairs of variables.
• Linear Graph Approach: For more complex systems, a linear graph is used to represent interactions between parameters. This graphical representation helps in understanding the relationships and potential impact of different parameter combinations.
• Statistical Analysis and Optimization: Advanced statistical methods are applied to analyze the results obtained from the test cases. This analysis helps in optimizing the test cases further, ensuring that subsequent rounds of testing are even more focused and efficient.
• Iterative Refinement: Based on initial testing outcomes, the orthogonal array and the test parameters can be refined iteratively. This process of continuous improvement helps in honing in on critical issues and optimizing test coverage over time.

## Which Tools are Used to Conduct Orthogonal Array Testing?

Some of the notable tools used in OAT include:

• OATools: A dedicated tool for OAT, OATools assists in creating orthogonal arrays and mapping test cases. For example, if a software requires testing various combinations of browser, OS, and user roles, OATools can generate an array that covers all possible combinations with minimal test cases.
• Pairwise Testing Tools (like PICT or ACTS): Tools like Microsoft's Pairwise Independent Combinatorial Testing (PICT) or NIST's Advanced Combinatorial Testing System (ACTS) are used for generating test cases that cover all pairs of input parameters. For instance, ACTS can be used to generate test cases for a mobile app considering factors like screen size, OS version, and app settings.
• Statistical Analysis Software (like Minitab or R): Software like Minitab or the R programming language provides robust statistical analysis capabilities. They are essential in analyzing the test results from orthogonal arrays, especially in identifying patterns and optimizing further tests. For example, R can be used to analyze user interaction data from a web application to optimize the test cases for user experience testing.
• Excel or Similar Spreadsheet Tools: For simpler applications, Excel can be used to manually create and manage orthogonal arrays. This is particularly useful in scenarios where the number of parameters and their levels are manageable without sophisticated software, like testing a small-scale e-commerce website's checkout process.

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## How to Perform Orthogonal Array Testing?

• Identify Testing Parameters: Determine the variables and factors to be tested.
• Select an Orthogonal Array: Choose an array that suits the number of parameters and levels.
• Map Test Cases: Use the array to map out combinations of parameters for each test case.
• Execute Test Cases: Run tests based on the combinations identified.
• Analyze Results: Evaluate the outcomes for defects or issues.

## What Challenges are Faced While Conducting Orthogonal Array Testing?

• Designing the test matrix requires a good understanding of statistical methods.
• OAT may not be suitable for all types of testing scenarios, particularly where the interaction of variables is not the primary concern.

## Advantages of Orthogonal Array Testing

• Maximizes test coverage with a minimal number of test cases.
• Reduces both the time and cost associated with testing.
• Particularly effective in identifying defects in critical parts of the system.

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## Disadvantages of Orthogonal Array Testing

• Implementing OAT effectively requires understanding of statistical concepts.
• May not be effective for all testing scenarios, especially those with fewer variables.

## Conclusion

Orthogonal Array Testing (OAT) stands out in software engineering for its efficient approach to handling complex systems with multiple variables. This method reduces the number of tests needed while ensuring thorough coverage, saving both time and resources.

While OAT requires some understanding of statistical concepts and may not suit every testing scenario, its effectiveness in critical system testing and adaptability with various tools make it a valuable strategy.

Contributed by: Shubham Kumar