Start automating your tests 10X Faster in Simple English with Testsigma
Try for freeTesting is a crucial part of the software development process to ensure the quality and performance of the software. In traditional testing, you typically create detailed test cases with expected outcomes. However, if you rely solely on assertions, your code can get really long. Every time you need new sets of assertions, it can become really confusing for you to test, especially complex objects or frameworks. This makes standard unit tests a bit tricky to handle, especially in dynamic environments. That is where approval testing comes in. It provides a different way of testing that makes things simpler. Approval testing captures and approves system outputs instead of dealing with complex assertions, making the testing process simpler.
In this blog, we will understand what approval testing exactly is, the benefits, how you can write approval testing, and what the best approval testing tools are. Let’s dive in deep and start with the approval testing definition.
Table Of Contents
- 1 What is Approval Testing?
- 2 Core Principles of Approval Testing
- 3 When is it Appropriate to Use Approval Testing?
- 4 When Not to Use Approval Testing?
- 5 What are the Benefits of Approval Tests?
- 6 Key Differences Between Approval Testing and Other Testing Methods
- 7 How to Write Approval Tests?
- 8 Approval Testing Process
- 9 How to Automate Approval Testing?
- 10 Approval Testing Application or Real-World Use Cases & Examples
- 11 Approval Testing Tools
- 12 Conclusion
- 13 Frequently Asked Questions
What is Approval Testing?
Approval testing is a way of testing where you capture the output of a software, system, or program and then compare it with the previously approved version to quickly verify that the current output matches the expected output. This can be beneficial in Agile development environments where frequent changes are expected.
This testing is mostly helpful where the expected output is complex or not easy to determine in a precise manner, such as graphical or large datasets. Hence, this testing becomes efficient and offers more flexibility in adapting to changes while providing a clear way to validate results.
Core Principles of Approval Testing
Approval Testing offers a refreshing alternative, built on a foundation of core principles that streamline the testing process and embrace change. Here’s a breakdown of the key principles that define Approval Testing:
- No Predictions: Unlike traditional unit tests where developers predict the exact output, Approval Testing captures the actual output of your code and compares it to a previously approved version. This eliminates the need for writing fragile assertions and makes it ideal for scenarios where predicting the output is complex.
- Embrace Change: This testing thrives in agile environments where code is constantly updated. When changes occur, the new output is presented for approval. If the change is intentional, the latest production becomes the new baseline. This reduces the maintenance burden of constantly updating tests for minor code modifications.
- Versatility: ThisTesting isn’t limited to a specific type of output. It can handle text, HTML, images, or any format your application generates. This makes it a powerful tool for testing your software’s user interfaces, reports, and other data-driven aspects.
By leveraging these core principles, Approval Testing offers a compelling approach to software testing:
- Reduced Maintenance: No more battling outdated assertions. Approval Testing adapts to changes, minimizing the time spent maintaining tests.
- Improved Developer Experience: Focus on writing good code rather than crafting intricate assertions. Approval Testing simplifies the testing process for developers.
- Enhanced Confidence: With a baseline of approved outputs, you can be confident that changes haven’t introduced regressions.
Incorporating these core principles into your testing strategy can significantly improve efficiency and empower you to deliver high-quality software with greater confidence.
When is it Appropriate to Use Approval Testing?
Approval testing comes with its own benefits where in several scenarios, manual or visible inspection is valuable, especially when the expected output is not predictable or varies over time.
Here are some of the scenarios where it is appropriate to use approval testing.
1. Stable Output: This testing is particularly suitable when dealing with stable outputs, providing a reliable method to verify and validate expected results against a predefined reference standard.
2. Data Transformation Processes: In data transformation processes, this testing can be used to confirm that the transformation logic is producing the expected output as the data evolves.
3. Test Legacy Systems: It is challenging to write traditional unit tests in legacy systems due to complex dependencies or lack of proper architecture. Hence, this testing can ensure that changes don’t introduce unintended side effects.
4. Third-party Integration Testing: When dealing with third-party APIs or systems where the expected output is not under your control, this testing can help capture and verify the responses.
5. Rapid Prototyping: During the prototyping phase, the system is expected to evolve quickly. In such cases, this testing can help quickly adapt to changes and ensure the new versions produce the expected results.
When Not to Use Approval Testing?
Even though approval testing is one of the best choices for testers in scenarios with well-defined and stable expected outputs, it may not be the optimal solution in highly dynamic environments or systems with non-deterministic behavior.
Let’s discuss some of the scenarios when it is not appropriate to use .
1. Mobile App Responsiveness: Mobile app responsiveness testing involves evaluating performance across various devices and network conditions. This testing may not adapt well to the diverse and dynamic nature of mobile environments.
2. Strict Compliance Requirements: In cases where strict compliance or regulatory requirements demand specific testing methodologies, you must go for other testing methods, which may be supplemented with this testing to meet these standards.
3. Complex Business Logic: If your system has intricate business logic where the output depends on a multitude of factors, traditional unit testing or behavior-driven testing might offer more precise validation instead of approval testing.
4. Critical Path Testing: Identifying and testing critical paths and dependencies require a dynamic testing approach that reacts to changes in the system’s flow. Approval testing, focused on static reference outputs, may not address the dynamic nature of critical path scenarios.
5. Real-time System: Approval testing is not ideal for real-time systems that need immediate responses, as the potential delays in the validation process and the overhead of updating reference outputs can hinder performance.
What are the Benefits of Approval Tests?
Explore various benefits of approval testing in this section, gaining insights into how this methodology enhances testing efficiency, fosters collaboration, and contributes to the reliability of software development processes.
1. Reduced Time for Writing Tests
This testing significantly reduces the time spent on writing tests by eliminating the manual updating of test cases with new expected outputs. Once an output is verified and approved, future test runs automatically compare results. However, you need to take precautions while updating approved files to prevent introducing incorrect results into the testing process.
2. Adaptable Output Verification and Clear Presentation
This testing proves to be beneficial for systems generating non-deterministic outputs, such as those involving graphical interfaces or complex data structures. It offers flexibility in output verification and ensures clarity in presenting test results.
3. Enhanced Collaboration
This testing facilitates seamless collaboration between developers and testers. By capturing and approving actual outputs, developers can endorse correct results, while testers can efficiently investigate and address any discrepancies that arise.
4. Efficient Handling of Large Data Structures
This testing is particularly beneficial for applications dealing with large and complex data structures. Approval tests simplify the whole validation process. Their ability to handle extensive output data makes them well-suited for scenarios where traditional testing methods may become difficult to execute.
Key Differences Between Approval Testing and Other Testing Methods
Approval Testing offers a unique approach to software testing, but how does it compare to traditional methods? Here’s a breakdown of the key differences between Approval Testing and other popular testing methods:
1. Unit Testing:
- Focus: Unit tests verify the functionality of individual units of code (functions, classes) in isolation.
- Approach: Developers write assertions that specify the expected output for a given input.
- Approval Testing vs. Unit Testing: Approval Testing captures and compares the actual output to an approved version. This is particularly helpful when predicting the exact output, which is difficult.
- Focus: Integration tests ensure different software components work together seamlessly.
- Approach: Tests simulate interactions between components to identify integration issues.
- Approval Testing vs. Integration Testing: Approval Testing can verify the combined output of integrated components, but it doesn’t replace the need for dedicated integration tests.
- Focus: Regression tests ensure that new changes have not introduced bugs that break existing functionalities.
- Approach: Regression tests typically involve re-running previously written unit and integration tests.
- Approval Testing vs. Regression Testing: Approval Testing can be a powerful regression tool. If the approved output changes after a modification, it indicates a potential regression.
4. Property-Based Testing:
- Focus: Property-based testing generates many inputs to verify the code behaves as expected under various conditions.
- Approach: Properties define expected code behavior; test cases are automatically generated based on these properties.
- Approval Testing vs. Property-Based Testing: Approval Testing focuses on verifying the output, while property-based testing focuses on exploring a wider range of inputs. Both methods can be complementary.
Understanding these key differences allows you to determine the most suitable testing approach for your needs. Approval Testing shines in scenarios where:
- Predicting exact outputs is challenging.
- The application generates complex outputs (e.g., UIs, reports).
- You want to streamline regression testing efforts.
How is Approval Testing Different from Assertion-Based Testing?
The choice between these approaches depends on the testing scenario and the nature of the software being tested. Approval testing is often used for scenarios where a tester needs to validate the output or behavior of the software. It is like looking at the output of a system and deciding if it is okay based on a saved example.
On the other hand, assertion-based testing is more automated and focuses on verifying specific conditions within the code, making it suitable for automated testing in various scenarios, especially in the context of software development. Assertion-based testing is like having a checklist that the computer follows automatically to make sure everything is working as expected, without needing a person to check each time.
How to Write Approval Tests?
Writing approval tests is all about ensuring the stability and reliability of software functionalities. Hence, this process involves creating a baseline or “approved” version of the output and subsequently comparing any changes against this baseline. The following steps provide a clear and structured approach for writing approval tests.
Step 1. Identify the Test Scenario: You have to figure out the functionality or the feature of the software that you want to test.
Step 2. Generate Initial Output: Run the test scenario and capture the output. Save this as the initial or “approved” version.
Step 3. Write Approval Test Code: You need to create a test script that executes the same scenario and generates the output. Then implement the logic to compare the new output with the approved version.
Step 4. Run the Approval Test: Execute the approval test to compare the current output with the approved version.
Step 5. Manual Review and Approval: The tester can manually review the results. If the output is as expected, then they can manually approve it. If the output has changed intentionally, update the approved version.
6. Iterate and Maintain: Testers need to rerun approval tests regularly as your software evolves. This is to ensure that the updated approved versions reflect intentional changes and are compatible enough with the ongoing changes.
Approval Testing Process
Approval testing involves a systemic and straightforward process with the following steps:
1. Record the Approved Output
Start by manually testing the system or component and noting down the expected output in a designated file, commonly a text file known as the “approved file.” This file acts as the reference point for subsequent tests.
2. Automate the Test
Develop an automated test script that runs the system or component with specific inputs, capturing the output generated during the test execution. This automation streamlines the testing process and ensures consistency.
3. Compare the Result with the Approved Output
After the automated test runs, you can compare the obtained output with the content stored in the approved file. A match indicates a successful test, while a mismatch signals a failure, suggesting a change in the system that needs immediate attention.
4. Review and Update the Approved File as Needed
In case of a test failure with expected changes, update the approved file to align with the new correct output. If the alterations are unexpected or incorrect, thorough investigation and corrective actions for the system or test are necessary.
By following these steps, approval testing provides a systematic and efficient way to validate outputs, ensuring the reliability of your software components.
How to Automate Approval Testing?
If you are looking to automate approval testing, Testsigma can help you to achieve this. As Testsigma comes with various products specially designed to suit your custom testing needs, it becomes easy to set up initial approval tests and seamlessly integrate automation into your workflow for efficient and collaborative testing processes.
1. Choose Testsigma Products
Testsigma offers various products for your testing requirements. Carefully begin the process by selecting solutions, such as – Testsigma Cloud for scalable and cloud-based testing, Testsigma Community for collaborative testing efforts, and Testsigma Enterprise for handling larger-scale projects with enterprise-level automation needs.
2. Set Up Initial Approval Tests
Initiate the process by manually recording and establishing expected outputs within Testsigma. These initial approval tests serve as the foundation for subsequent automated testing.
3. Automate with Testsigma Features
Leverage the automation capabilities of Testsigma Cloud or Testsigma Enterprise to create robust automated test scripts.
Assertions can be setup to approve the tests automated. Or visual testing can be used to approve tests according to the visual baselines created.
Execute these scripts to perform systematic testing of your system or software, capturing and comparing outputs efficiently.
4. Integrate with CI/CD
Seamlessly integrate Testsigma into your Continuous Integration/Continuous Delivery (CI/CD) pipeline. This ensures that approval tests are automatically triggered with each code change, providing continuous validation and early detection of potential issues.
Document best practices for maintaining and updating approved files within your team. Share guidelines that facilitate effective collaboration, ensuring that team members are aligned on procedures and practices for seamless integration of Testsigma into the testing workflow. Regularly update this documentation to keep the team informed of any evolving practices or features.
Approval Testing Application or Real-World Use Cases & Examples
Approval Testing isn’t just a theoretical concept;
Do you know it’s a practical tool that tackles challenges across diverse software development projects?
Here’s a dive into how this Testing empowers developers and testers in real-world use cases:
1. Database Schema Migrations: Ensuring Smooth Transitions
Database schema changes are essential for evolving applications. However, these migrations can sometimes break compatibility with existing code. Approval Testing steps in to streamline this process.
Imagine this: You’ve created a migration script to update your database schema. With Approval Testing, you can visually compare the before-and-after outputs of queries (table structures, data). The migration gets the green light if everything looks good, reducing the risk of unexpected compatibility issues.
2. API Testing: Verifying Every Response
APIs are the backbone of modern applications, and ensuring their responses are accurate is crucial. Approval Testing excels in this domain.
Here’s how it works:
When you create tests that make API calls and grab the response data (JSON, XML), Approval Testing checks this response against a previously approved version. If the response format or data structure changes as planned, the new response becomes the standard.
This method simplifies API testing and helps you catch any unintentional changes in the response.
3. Financial Systems: Precision is Paramount
Financial systems demand unwavering accuracy. Approval Testing provides a safety net for critical outputs.
Let’s say you’re working on a stock trading platform. Approval Testing can verify calculations, reports (trade summaries, account statements), and other visualizations. By comparing the generated outputs to pre-approved versions, you can ensure precise calculations and user interfaces accurately reflect the data.
4. Game Development: Maintaining Consistency in Dynamic Worlds
Games rely on consistent behavior to deliver an enjoyable experience. Approval Testing helps maintain this consistency, especially when randomness is involved.
Imagine you’re playing a game where the levels are made up on the fly. With Approval Testing, you can take a snapshot of these random levels. Developers can check them out and give the thumbs up, ensuring the levels are manageable and easy and look just right. This way, the game feels just as good no matter how often you play.
These are just a few examples of how Approval Testing empowers developers and testers in diverse scenarios. By leveraging its core principles, you can streamline testing efforts, improve code maintainability, and deliver high-quality software with greater confidence.
Approval Testing Tools
Approval testing tools streamline the verification process of software outputs by comparing actual results to approved baselines. In addition to Testsigma, below are some tools that help with approval testing.
1. TextTest
TextTest is a free tool designed for testing the functionality of text-based programs. It operates by running a program in various ways through the command line and uses the text output to control the application’s behavior.
Unlike many current acceptance testing frameworks that rely on manually written assertions, TextTest compares the plain text output generated by programs with a predefined ‘gold standard.’ For Python and Java GUI testing, StoryText, an open-source tool, complements TextTest seamlessly.
2. Jest
Jest, created by Facebook, is an open JavaScript testing tool ideal for React projects. It is more convenient for ensuring that your UI does not change abruptly.
In a typical snapshot test for a mobile app, Jest renders a UI component, captures a screenshot, and checks it against a stored reference image. If the images differ, it signals either an unexpected change or the need to update the screenshot to match the new version of the UI component.
3. Diffblue Cover
Diffblue Cover is an advanced tool that automates unit testing for Java applications, utilizing artificial intelligence to generate comprehensive test cases.
While not a conventional approval testing tool, its automated approach aligns with approval testing principles by validating code behavior and integrating seamlessly into CI/CD pipelines, contributing to code reliability and minimizing the manual effort required for testing.
Conclusion
Through this blog, we have learned that this testing emerges as a powerful and flexible approach, offering a streamlined alternative to traditional testing methodologies. As we’ve explored the benefits, scenarios, and how-to aspects of approval testing, it is evident that this methodology enhances testing efficiency and adapts well to scenarios where expected outputs are challenging to define precisely.
For seamless automation and integration into your testing workflow, consider exploring Testsigma, a comprehensive testing solution tailored to your custom needs. Elevate your testing practices and embrace the efficiency and collaboration that approval testing brings to the forefront. Make your testing journey smoother with Testsigma today.
Frequently Asked Questions
How can I start using approval testing tools?
You can start by applying approval testing via tools to legacy features in your application for initial coverage. As you make changes, integrate more functional acceptance tests alongside them for a more robust testing strategy.
What is the Difference Between a Unit Test and an Approval Test?
The main difference between a unit test and an approval test lies in their focus and testing approach. Whereas a unit test is designed to validate the functionality of a unit or component of code in isolation, approval tests are broader and focus on the overall behavior of a system or component, capturing and validating its output.
In short, unit tests are commonly used during the development phase to verify individual code units. Approval tests are particularly beneficial for complex or non-deterministic scenarios where precise output expectations are challenging to define in advance.