Refactoring AI Product Tests Addressing Mocking Issues

by Sharif Sakr 55 views

Hey everyone, we've got some crucial work ahead of us concerning the AI product tests. Our current tests in test_ai_product_features.py are facing some hurdles in the CI environment due to incorrect mocking. Let's dive into the specifics, explore the proposed solutions, and figure out the best path forward to ensure our AI product functionality is thoroughly tested.

Understanding the Problem: Why Are Our Tests Failing?

The core issue lies in the way we're trying to mock functions. Several tests are attempting to mock functions that simply don't exist in the modules where they're being called. This leads to test failures and incomplete coverage, which isn't ideal for maintaining a robust and reliable product. Specifically, we've identified the following failing tests:

1. TestAIProductService::test_create_product_from_description

This test stumbles because it's trying to mock ai_product_service.get_adapter_class, a function that doesn't actually exist. Properly testing this functionality is crucial for ensuring our AI can seamlessly create products from descriptions. We need to refactor this test to accurately simulate the behavior of the adapter, which plays a vital role in translating product descriptions into concrete product specifications. The adapter class is the key to integrating our AI models with the product creation process. Therefore, a robust test suite for this component is essential. The current approach of mocking a non-existent function is not only ineffective but also masks potential issues within the adapter's implementation. By addressing this, we can gain confidence in the reliability of our AI-driven product creation pipeline.

2. TestProductAPIs::test_product_suggestions_api

Here, the test aims to mock admin_ui.get_industry_specific_products. However, the function is actually imported from default_products inside the route functions. This misdirection means our mock isn't intercepting the correct call, leading to an inaccurate test. The product suggestions API is a critical feature for our users, providing them with tailored recommendations based on their industry. Ensuring this API functions correctly is paramount for user satisfaction and engagement. The test's failure to properly mock the underlying function prevents us from thoroughly validating the API's behavior under various scenarios. Refactoring this test will involve correctly identifying the source of the function being called and adjusting the mock accordingly. This will enable us to create a more reliable test that accurately reflects the API's functionality.

3. TestProductAPIs::test_quick_create_products_api

This test suffers from the same problem as the previous one. It incorrectly tries to mock admin_ui.get_default_products, while the function is imported from default_products. Getting this right is key to testing our quick product creation feature effectively. The quick create products API streamlines the process of adding new products to our system, making it essential for efficient product management. A faulty test in this area can lead to undetected bugs and potential disruptions in the product creation workflow. By addressing the incorrect mocking, we can ensure that this test accurately validates the API's ability to quickly and reliably create products. This will contribute to a smoother user experience and reduce the risk of errors.

4. test_ai_integration

This test also makes the same mistake as test 1, attempting to mock ai_product_service.get_adapter_class, which doesn't exist. A comprehensive integration test is crucial for verifying the interaction between different components of our AI product system. This test aims to ensure that all the pieces of the puzzle fit together seamlessly, from the AI model to the product creation pipeline. The incorrect mocking, however, prevents us from achieving this goal. By refactoring the test to accurately simulate the behavior of the adapter, we can gain a more holistic view of the system's functionality. This will enable us to identify and address any integration issues that may arise, ensuring that our AI product works harmoniously as a whole.

Current Workaround: Skipping Tests in CI

For now, we've implemented a temporary workaround in our CI workflow to skip these failing tests. This allows us to proceed with other builds and deployments while we address the underlying issues. The workaround looks like this in our pytest configuration:

pytest test_ai_product_features.py -k "not test_create_product_from_description and not test_product_suggestions_api and not test_quick_create_products_api and not test_ai_integration"

While this prevents the tests from failing our builds, it's not a long-term solution. We need to refactor these tests to ensure proper test coverage and confidence in our AI product features.

Proposed Solutions: How Can We Fix This?

We've brainstormed a few options to tackle these mocking issues and improve our test suite. Let's explore them:

1. Option 1: Refactor Tests to Mock at the Correct Level

This option focuses on adjusting the mocks to target the correct functions and modules. It involves:

  • Mocking default_products.get_industry_specific_products instead of admin_ui.get_industry_specific_products. This ensures we're intercepting the actual function being called.
  • Creating proper test doubles for adapter functionality. This means building mock objects that accurately simulate the behavior of the adapter without relying on the real implementation. This approach involves carefully examining the call stack and identifying the precise location where the function being tested is invoked. By targeting the mock at this specific point, we can ensure that the test accurately captures the behavior of the code under scrutiny. This level of precision is crucial for avoiding false positives and negatives, and for gaining a true understanding of the system's functionality. Furthermore, creating proper test doubles for adapter functionality allows us to isolate the unit being tested and eliminate external dependencies. This isolation is essential for conducting unit tests that are focused, repeatable, and reliable. By crafting mock objects that precisely mimic the behavior of the adapter, we can thoroughly validate the code that interacts with it, ensuring that it functions as expected under various scenarios.

2. Option 2: Refactor the Code to Be More Testable

This option takes a broader approach by modifying the code itself to make it easier to test. This includes:

  • Moving imports to module level in admin_ui.py. This makes the dependencies more explicit and easier to mock.
  • Creating a proper adapter factory function that can be mocked. This provides a centralized point for creating adapters, allowing us to substitute a mock adapter in tests.
  • Considering dependency injection for better testability. This involves passing dependencies as arguments to functions or classes, making it easier to swap in mock implementations. This strategy not only simplifies testing but also enhances the overall maintainability and flexibility of our codebase. By moving imports to the module level, we gain a clearer understanding of the dependencies required by each module. This explicitness makes it easier to identify potential issues and refactor the code in the future. Creating an adapter factory function provides a controlled mechanism for creating adapter instances, enabling us to seamlessly substitute a mock adapter during testing. This isolation ensures that our tests are focused and independent, reducing the risk of unintended side effects. Dependency injection takes this concept a step further by allowing us to inject dependencies into functions or classes, making them more adaptable and testable. This approach promotes loose coupling and modularity, making our code easier to reason about and maintain. Ultimately, refactoring the code for testability is an investment in the long-term health and resilience of our system.

3. Option 3: Integration Test Approach

This option shifts our focus from unit tests to integration tests, which test the interaction between different parts of the system. It involves:

  • Using a test database with fixtures. This provides a consistent and isolated environment for testing.
  • Testing the full stack rather than mocking internals. This gives us a more realistic view of how the system behaves in production.
  • This approach results in more realistic but slower tests. Adopting an integration test approach can provide valuable insights into the overall behavior of our system, but it's crucial to weigh the benefits against the potential drawbacks. Integration tests, by their nature, involve multiple components working together, which means they can uncover issues that might be missed by unit tests. This holistic perspective is particularly valuable for complex systems where interactions between different modules can be subtle and difficult to predict. However, integration tests also tend to be slower and more resource-intensive than unit tests. Setting up a test database, populating it with fixtures, and running tests against the full stack can take considerably longer than running isolated unit tests. This slower feedback loop can impact development velocity and make it more challenging to identify the root cause of failures. Therefore, a balanced approach is often the most effective, combining the speed and precision of unit tests with the broader scope of integration tests.

Related PRs: Context and History

To understand the context behind these tests, it's helpful to look at the related pull requests:

  • #19 - Original AI product setup feature
  • #20 - Critical fixes for product creation

Reviewing these PRs can give us insights into the original intent of the tests and the evolution of the AI product features.

Priority: Medium

We've classified this issue as a medium priority. The AI product feature is functional, but the incomplete test coverage means we have less confidence in its long-term stability. Addressing these mocking issues is essential for ensuring the reliability and maintainability of our AI product.

Next Steps: Let's Get This Fixed!

So, what's the plan, guys? Let's discuss these proposed solutions and decide on the best approach to refactor these tests. Which option do you think provides the most value in terms of test coverage, maintainability, and development effort? Let's get this sorted out so we can have a robust and reliable AI product!