Test Collateral Deserialization: Dependency Injection Guide

Aug 15, 2025 by Benjamin Cohen 60 views

Test Collateral Deserialization with Dependency Injection

Hey guys! Today, we're diving deep into a crucial aspect of our testing strategy: collateral deserialization. We'll be discussing how to improve our unit tests by using dependency injection instead of relying on external Phala endpoints. This approach will make our tests more reliable, faster, and easier to debug. Let's jump right in!

Background: The Current Challenge

Currently, we have a test case, test_upload_quote_for_collateral_with_phala_endpoint, that's designed to verify the logic within our upload_quote_for_collateral function. This function is super important because it handles the process of uploading collateral quotes. However, the test relies on making actual HTTP requests to a Phala endpoint. While this might seem straightforward, it introduces several problems that we need to address.

First off, the test fails if you're working offline. Imagine you're on a plane or in a coffee shop with spotty Wi-Fi – your tests will be unreliable. This is a major pain point for developers who need to run tests in various environments. Secondly, Phala's caching mechanism can cause inconsistencies. Updates on the Phala API might not be immediately reflected in our test results, leading to false positives or negatives. This makes it difficult to ensure our system is truly behaving as expected.

Most importantly, it's not entirely clear what we're actually testing. We're not trying to verify the Phala API itself; that would be a separate test altogether. Our main goal is to ensure that our system can correctly deserialize the response from the Phala API, assuming it behaves as expected. In other words, we need to isolate our logic and test it independently of external factors. Focusing on testing our logic ensures that we are building a robust and reliable system. We want to make sure that if Phala sends us the right data, we can handle it correctly. By decoupling our tests from external dependencies, we make them more focused and easier to maintain.

The Solution: Dependency Injection

The best way to tackle these issues is to refactor the upload_quote_for_collateral function to embrace dependency injection. What exactly does that mean? Simply put, it means allowing us to “inject” the HTTP dependency into the function, rather than having the function create it internally. This gives us the flexibility to provide a mock HTTP response in our test case. Think of it like this: instead of relying on a specific restaurant for food, we can bring our own (mock) food to the party.

By implementing dependency injection, we gain a ton of control over our testing environment. We can simulate various scenarios and ensure our code handles them gracefully. This also means that we can test the core logic of our function in isolation, without worrying about network connectivity or external API behavior. Dependency injection makes our tests more predictable and easier to reason about.

User Story: A Developer's Perspective

Let's put ourselves in the shoes of a developer. As a developer, I want our unit tests to focus squarely on testing our own logic. I want them to work offline, so I can develop anywhere, anytime. I need confidence that my code works, regardless of external factors. This is why dependency injection is such a powerful tool. It allows us to create tests that are reliable, focused, and independent.

We've all been there – wrestling with flaky tests that fail intermittently due to network issues or API outages. It's frustrating and time-consuming. By embracing dependency injection, we can eliminate these headaches and focus on what truly matters: building great software. Writing tests that focus on our code’s logic helps us identify bugs early and often. This means less time debugging and more time building awesome features.

Acceptance Criteria: Achieving Our Goal

Our acceptance criteria are clear: the test_upload_quote_for_collateral_with_phala_endpoint test case should be executable offline, while still providing comprehensive coverage of the logic within the upload_quote_for_collateral function. This means we need to be able to mock the HTTP response and verify that our function correctly deserializes the data. We want to ensure that our tests are robust and reliable, no matter the environment.

To achieve this, we'll need to modify the upload_quote_for_collateral function to accept an HTTP client as a parameter. This will allow us to pass in a mock HTTP client during testing, which will return a predefined response. We can then assert that our function correctly processes this response. The key is to separate the act of making an HTTP request from the logic of handling the response.

Diving Deeper: Benefits of Dependency Injection

Let's explore some more reasons why dependency injection is a game-changer for our testing strategy:

Improved Testability: As we've discussed, dependency injection makes our code much easier to test. We can isolate units of code and test them independently, without relying on external resources or services.
Increased Reliability: By eliminating external dependencies, we make our tests more reliable and less prone to failure. We can run our tests with confidence, knowing that they will produce consistent results.
Faster Execution: Mocking external dependencies allows our tests to run much faster. We don't have to wait for network requests or external API calls to complete.
Simplified Debugging: When a test fails, it's much easier to pinpoint the cause when we're testing a single unit of code in isolation. We can quickly identify whether the issue lies within our logic or in an external dependency.
Enhanced Code Design: Dependency injection encourages us to write more modular and loosely coupled code. This makes our codebase more maintainable and easier to extend.

How to Implement Dependency Injection

So, how do we actually implement dependency injection in our code? There are several approaches we can take, but the basic idea is always the same: instead of creating dependencies within a function or class, we pass them in as parameters. Let's look at a simplified example:

def upload_quote_for_collateral(http_client, ...):
    response = http_client.get(...) # Make the http call
    # Process the response
    ...

In this example, the upload_quote_for_collateral function accepts an http_client as a parameter. This allows us to pass in a real HTTP client in our production code, and a mock HTTP client in our test code. Here's how we might use a mock HTTP client in a test:

class MockHttpClient:
    def get(self, url):
        # Return a predefined response
        return MockResponse(...)

http_client = MockHttpClient()
result = upload_quote_for_collateral(http_client, ...)
assert ... # Assert the expected behavior

As you can see, we've created a MockHttpClient class that returns a predefined response. This allows us to simulate the behavior of the Phala API without actually making an HTTP request. Using mock objects in our tests provides predictable results. We can be confident that our code is behaving as expected, even if the Phala API is unavailable or behaving unexpectedly.

Resources & Additional Notes

Currently, there are no specific resources or additional notes provided for this task. However, we can leverage existing documentation and examples on dependency injection and unit testing to guide our implementation. There are countless resources available online, including tutorials, blog posts, and library documentation. Leveraging existing resources helps us learn and grow as developers. We can build upon the knowledge of others and avoid reinventing the wheel.

Conclusion: Embracing Testable Code

By adopting dependency injection, we can significantly improve the quality and reliability of our tests. We'll be able to focus on testing our core logic in isolation, without being hampered by external dependencies. This will lead to faster, more reliable tests, and a more robust system overall. Writing testable code is a key aspect of building high-quality software. It allows us to catch bugs early, reduce debugging time, and increase our confidence in the system.

So, let's get started! By refactoring the upload_quote_for_collateral function to use dependency injection, we'll be taking a big step towards building a more testable and reliable system. Embracing dependency injection is an investment in the future of our codebase. It will pay dividends in the form of increased stability, maintainability, and developer productivity.