Contract Testing Tutorial: Comprehensive Guide With Best Practices

Contract testing is an approach to determine how different components within one application system should interact. Teams use this information to create virtual contracts that specify how two microservices should interact. In the future, this contract will serve as a benchmark for testing microservice interactions.

It has been a decade since the tech giants, including Amazon, Facebook, and Google, have been leveraging contract testing for handling their microservices testing. But that doesn't mean it is exclusive only to high-tech MNCs. Any business that deals with applications can benefit it.

Contract testing is a technique that checks every app separately to test an integration point. In other words, it's a testing process that analyzes whether integration points follow a standard contract or agreement in a highly distributed environment.

The aim is to ensure that the messages it communicates adhere to a shared understanding in a well-documented contract.

The code generates the contract and assures timely upgrades and continuous monitoring based on an application’s customization needs and real-time improvements. It helps keep on par with customer and market demands and ensures that no microservice system escapes individual testing.

The core purpose of contract testing is to assist with integration tests. In simple terms, it is essential because integration testing has some loopholes that can cause testers and developers enormous amounts of inconvenience.

Speaking of which, integration testing helps instill confidence that a system flawlessly works as a single unit. It also helps validate that the remotely communicating parts work in cohesion, such as mobile applications, web applications, and microservices.

End-to-end integrated tests, the most common type of integrated tests, have some painful cons. Since they traverse different systems, testers usually run them serially, which takes up a lot of time.

They might also have to create a prerequisite setup like data preparation which can further slow them down. Not only that, but they are also a challenge to maintain and pose a risk of unreliability or flakiness. The remote and distributed nature of the issue makes matters worse.

Things can further entangle with more code getting tested, and along comes an exponentially slower running of test suites. This results in clogged releases in test automation pipelines, leading to a lack of scalability. Of course, when a process takes its own time to find bugs and uncovers them in the later stages, the costs can increase indefinitely, even in the current agile ecosystem.

That is where contract testing comes into the picture. This methodology ensures flawless communication and high compatibility between two independent systems, such as microservices.

By capturing these interactions and making a mandate for the parties involved to be on the same page on the permitted set of interactions, contract testing makes a lot of room for constant evolution. The best thing about this testing is that the QA team can achieve the same outcome as integration testing without all the challenges involved.

Even though contract tests eliminate many challenges with integration testing, the benefits don't end there. In the section, we are going to take a look at some other advantages of contract tests in a broader sense.

Let's check them out

• It facilitates independent releases and makes confident continuous delivery a cakewalk.
• These tests are quick and low maintenance, which leads to a high ROI. Since developers can easily and quickly experiment with different tests, understand various requirements, and identify endpoints, it eliminates a lengthy process and going back and forth multiple times.
• Another factor contributing to the high speed is its ability to create an isolated coding environment instead of deploying a dev one.
• Contract tests are repeatable and highly scalable.
• Consumers can easily understand the behavior of a service with the help of mocks and tests that facilitate learning. This also reduces infrastructure costs and operational expenses. Moreover, since old service revisions are always handy, testing new enhancements won't incur additional costs.
• Providers can run these tests against real and mock systems to check whether responses match and certify brand new contracts.
• It also shortens the duration of the feedback loop by eliminating the wait time for regression results. Thanks to an isolated environment, developers can quickly locate business flow and functional issues.
• You can easily reuse test scripts and artifacts which further contributes to an accelerated test execution timeline for different testing scenarios. Testers also catch bugs in the earlier testing process.
• Before pushing code, run contract tests on developer machines to uncover local bugs.
• These tests develop consumers like a React web app before moving on to the API.
• It offers the ability to locate precisely what fields users are interested in, allowing unused ones to be removed. As a result, adding new fields to the provider API is pretty simple and will not affect the user.

Suppose you're planning on inculcating a deeper grasp on contract tests. Here are some important terms you should know.

• Consumer: A consumer usually refers to a client who desires to receive data in a contract between two entities. This component initiates the HTTP request for the other component
• Provider: It refers to an entity like an API, offering the data required by the client on a server. The provider is responsible for responding to an initiated HTTP request from the consumer
• Contract file: A contract file is a file that comprises JSON responses and requests in consumer tests in a serialized fashion
• Service agreement: It is a commitment from the service provider’s end that promises the return of a specific response as soon as the consumer raises a request.

Contract testing gives the best results when testers use it with compatible environments such as API. Specific dependencies and information rely on services you should look for after performing a feasibility check. It tests the integration point between different services, such as microservices, APIs, etc.

The most common uses of it include:

• Monitoring a consumer's workflow for irregularities,
• Determining if there are any bugs or defects in the service configuration,
• Keeping the connections secure even when the service configuration is changed by the producer.

There are two main types of contract tests- consumer-driven and provider-driven. Let us take a look at both.

• Consumer-driven: It refers to a pattern that only tests parts of communication the consumer actually uses. Meaning, the provider behavior not in use by present consumers can change without causing any breakage in tests.
• Provider-driven: It refers to the method in the context of a single provider application instead of integration. In this case, contract testing usually refers to a process that ensures conformance of the provider’s behavior to the well-documented contract. For instance, this could be an OpenAPI document.

Thus, it helps eliminate integration failures by syncing the documentation and the provider code. However, this method alone isn't a foolproof solution to avoiding integration bugs as it might not fulfill all consumers' expectations.

Consumer-Driven Contract Testing (CDCT) follows a clear lifecycle where the consumer defines expectations and the provider proves it meets them. The four steps below show how a contract flows from consumer to provider:

• Consumer writes a test against a mock provider: using a framework like Pact, the consumer team writes a test describing the exact requests it makes and the responses it expects, running it against a local mock of the provider.
• The test generates a contract file: running that test produces a contract, usually a JSON file, that records every interaction the consumer relies on.
• The consumer publishes the contract to a shared broker: the JSON contract is pushed to a shared repository such as a Pact Broker, which versions it and makes it available to the provider.
• The provider verifies against the contract: the provider pulls the contract from the broker and replays it against its real, running service. If every expected interaction passes, the provider is compatible; if not, the build fails before the change ships.

The flow, at a glance:

Consumer test (mock)  ->  JSON contract  ->  Pact Broker  ->  Provider verification
     writes expectations      generated           shared/versioned      replays & confirms

Because the consumer drives the contract, the provider only has to honor what consumers actually use, and any breaking change surfaces at the verification step instead of in production.

These three methodologies are easy to confuse because they all touch services and APIs, but they check different things. The table below contrasts them across the dimensions that matter when you decide which to run:

In short, contract testing checks that two services still agree on their interface, API testing checks that one service behaves correctly, and integration testing checks that everything works once assembled. They complement rather than replace each other.

In this section, we will discuss some scenarios where contract tests can be helpful.

• Microservices: It is one of the common use cases. In most organizations, software versioning isn't taken seriously until the software development life cycle ends. The same applies when implementing a microservice.

Since microservices typically have many components, maintaining them can become increasingly difficult due to:

• Failure to integrate, lack of documentation, and versioning.
• As testing scales, complexity increases.
• API: By implementing contract tests, API producers can sync with consumers.
  • Consumers prepare all test cases and store results in computer programs.
  • The producer verifies the test case. Upon completing the verification process, a consumer communicates with API producers.

Overall, contract testing can help you with

• Establishing the correct interaction between a producer and a consumer.
• Monitoring the responses of consumers and producers.
• Increasing the compatibility of microservices.

When it comes to running contract tests, there are three ways to do it:

• Testing against a deployed service: It's a good idea to test against a deployed service to make sure that services (such as an API provider and a client) can still interact with each other after changes are made.
• Testing against a mocked service: Testing against a mock object can be a helpful alternative to time-consuming integration tests. While it cannot replace integration testing entirely, contract tests is still an important part of the development process.
• Testing against a to-be service: If you work in a team that follows test-driven development (TDD), then writing tests as you go along can help you discuss and make design decisions. After the endpoints are written, the tests will pass and verify that the provider has fulfilled its contract.

• Specmatic: Specmatic is a contract testing framework that enables developers to adopt contract-driven development (CDD) to build and deploy microservices and microfrontends with confidence and efficiency. By using Specmatic, development teams can streamline their processes, reduce dependencies and accelerate the deployment of independent services.
• Pact: It is a command-line tool that offers faster feedback time. Pact helps producers and consumers communicate more easily.
• Spring cloud contract: This tool can be used with the Java Virtual Machine (JVM) but can also be extended to work with other environments. Spring cloud contract can mainly help you perform producer-driven contract tests.
• Hoverfly: Hoverfly is written in Go and has native support for Java, enabling it to be run inside JUnit tests. It can be used to test both REST APIs and calls between microservices.

Other than the tools mentioned above, it is important to implement an end-to-end automated testing management practice. It will help your organizations leverage the potential of contract tests.

Test orchestration and execution platforms like TestMu AI provide a scalable test infrastructure to perform manual and automated testing of your web and mobile applications across an online browser farm of 3000+ real browsers, devices, and operating systems.

This will help your business achieve organization-leading quality objectives.

With TestMu AI’s test automation cloud, you can run automated tests using frameworks like Selenium, Cypress, Playwright, Appium, and more. Furthermore, with parallel testing, you can cut down build times by multiple folds

Pact and Postman are often mentioned together, but they solve different problems. Postman is primarily a functional API testing tool: you send real requests to a live endpoint and assert on the actual response, which is great for exploring and validating an API's implementation. Pact is a contract testing framework: it ensures a consumer and provider agree on their interface in isolation, without deploying the full API.

• Postman: tests the real, running implementation of an endpoint. Best for functional checks, exploratory testing, and monitoring live APIs.
• Pact: generates and verifies a consumer-driven contract so both sides stay compatible even when tested separately. Best for catching breaking changes between microservices before deployment.

In practice teams use both: Postman to confirm an endpoint works, and Pact to guarantee that a change to one service will not break the consumers that depend on it. Provider-driven contracts can also be derived from an OpenAPI Specification, giving another way to keep documentation and behavior in sync.

Frameworks like Pact generate and verify the contracts, but running that verification reliably at scale is where a cloud execution platform helps. TestMu AI complements a contract testing setup rather than replacing it:

• Orchestrate contract tests in CI/CD: trigger consumer test generation and provider verification as pipeline stages so a broken contract fails the build automatically.
• Run provider verification in parallel: execute large provider verification suites concurrently on TestMu AI's HyperExecute test orchestration to keep feedback fast as the number of consumers grows.
• Validate the consumer UI against mocked contracts: test the front end (for example a React or Angular app) against mocked contract endpoints across real browsers, confirming the UI still works with the agreed response shape.

This pairs the correctness of contract testing with the scale and speed of cloud execution, so both the API interface and the application that consumes it stay verified on every change.

Even though contract testing has numerous benefits for a business, it does come with its own set of unique challenges. A few of them are as follows.

• Need to run plenty of individual tests for evaluating various microservices within an application.
• Even though mocks can reliably mimic the behavior of a microservice without actually running it, there isn't a hard and fast rule that guarantees the same behavior of a microservice in production.

However, one way to curb these limitations is to update the contracts frequently. You can accomplish this goal by keeping all development teams in the loop during the contract test process to clearly define new requirements whenever team members modify a microservice's behavior or functionality.

This way, the contracts will keep accurately reflecting the behavior desirable from microservices.

Contract testing started as a buzzword within a bunch of discussion boards. But in the current testing scenario, it has emerged as one of the most promising processes for the progress of a business.

We already know how software testing has emerged as a prominent pillar to impart a seamless virtual experience for businesses. The primary focus is on the versatility, performance, and speed of these applications. While these factors are crucial, a QA team must also ensure the long-term legitimacy and sustainability of the traits mentioned above.

With tons of business logic developments involving adjusting old tests and writing new ones, traditional testing cannot scale services to their full potential. Most organizations have been switching to a relatively distributed architecture and have successfully eliminated obstacles such as service misalignment.

Depending on the technical feasibility, cost estimates, and unique project requirements, you can opt for contract testing and prevent a fair amount of feedback, time, and production failure probabilities.

Austin Siewert

Co-Founder, Steadfast Systems

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