How do you use data-driven or keyword-driven testing approaches for regression testing?

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Regression testing is the process of verifying that the existing functionality of a software system is not affected by changes or updates. It is a vital part of software quality assurance, but it can also be time-consuming and repetitive. To make regression testing more efficient and effective, you can use data-driven or keyword-driven testing approaches. These are two types of automation techniques that allow you to separate the test data and the test logic from the test scripts. In this article, you will learn how to use these approaches for regression testing and what are their benefits and challenges.

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Corey Holt

Software Quality Analyst & Engineer
Stephen Platten

Award Winning Passionate Tester & QA ??| Embracing & Advocating Stoicism | Principal Consultant @ Inspired Testing |…
Shruti Pandey

Podcaster || Independent Consultant || Solutions Engineer || T-Shaped Developer || Automation Enthusiast || Tester

1 Data-driven testing

Data-driven testing is an approach that uses external data sources, such as databases, spreadsheets, or files, to provide the input and expected output for the test cases. This way, you can run the same test script with different sets of data without modifying the code. Data-driven testing is useful for regression testing when you need to verify the same functionality with multiple scenarios or variations - like checking the login functionality of a web application with different user credentials and roles. To implement data-driven testing, you should identify the test scenarios and their data requirements, create or select a data source and populate it with the test data, design a test script with variables or parameters to access the data from the source, run the test script and compare the actual results with the expected results from the data source, and analyze the test results and report any defects or issues.

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Corey Holt

Software Quality Analyst & Engineer
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I really like this approach from a maintenance perspective. This allows test scenarios to be less dependent on the data allowing for more reuse and less bloat in the test scenario repository. When the repositories get too large or too data dependent, the ability to keep test scenarios up to date with the application becomes more difficult, while divorcing the two, when possible, can help alleviate that issue.

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Sangita M.

Visionary AI Strategist | Harnessing AI technology to solve real-world business challenges.
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Data-driven testing involves using multiple data sets to validate test cases in regression testing. Keyword-driven testing utilizes action words to create reusable test scripts, ensuring comprehensive coverage with minimal manual effort in regression testing.

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2 Keyword-driven testing

Keyword-driven testing is an approach that uses keywords or commands to define the actions and validations for the test cases. This approach stores the keywords, test data, and object identifiers in external tables or files, allowing users to create and execute test cases without writing code. It's especially useful for regression testing complex or dynamic functionality with different steps or flows, such as testing the checkout process of an e-commerce website with various products and payment methods. To implement keyword-driven testing, you need to identify the test scenarios and associated keywords, data, and objects; create or select the keyword table or file; design a test script that uses a keyword interpreter or engine; run the script and compare actual results with expected results; and finally analyze the test results and report any defects or issues.

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Stephen Platten

Award Winning Passionate Tester & QA ??| Embracing & Advocating Stoicism | Principal Consultant @ Inspired Testing | The Stoic Tester |
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We are trialling using AI to scan for keywords in defect reports. Then cross matching those keywords with test cases. Helping us to understand defect clusters and test coverage. Example: 100 defects, 30 have the same keyword 100 corresponding test cases, 3 also have the same keyword We need to expand out test case coverage around that area.

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3 Benefits and challenges

Regression testing can be done using a data-driven or keyword-driven approach, each of which offer certain benefits and challenges. The benefits include reducing maintenance and duplication of test scripts, increasing reusability and scalability of test cases, improving readability and understandability of test cases, and enhancing collaboration and communication among testers, developers, and business analysts. Challenges include needing additional tools and frameworks to support data or keyword extraction and execution, requiring extra effort and time to create and manage the data or keyword sources, introducing errors due to data or keyword mismatches or changes, and not covering all possible scenarios or variations of the functionality under test.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Shruti Pandey

Podcaster || Independent Consultant || Solutions Engineer || T-Shaped Developer || Automation Enthusiast || Tester
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Way back in 2012/13, when my team was evaluating automated testing solutions for Salesforce, we stumbled onto RobotFramework, which is a keyword-driven framework. It suited us best because it turned out a great solution for us to automate salesforce and being keyword driven, it was very easy to get started with. We later added data-driven approach on the framework, making it a Hybrid testing approach for our regression and it helped us a great deal in saving time, effort and helped us in facilitating our Bi-weekly "agile" styled regression cycles. So, depending on your project, product and requirements, a hybrid way combining both Keyword-driven and Data-driven approaches can be really the best solution -- Shruti Pandey

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How do you use data-driven or keyword-driven testing approaches for regression testing?

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1 Data-driven testing

2 Keyword-driven testing

3 Benefits and challenges

4 Here’s what else to consider

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