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test-pipeline is a small (very small!) library that can be used to improve your Clojure (or ClojureScript) test suite.

The central idea is that tests can be implemented as a series of reusable, composable, cooperating steps.

A context map is passed to each step function, which may add or change keys in the context map before passing it to the next step. This may be familiar: the same pattern shows up in Ring as middleware, where each function passes a request map to the next function.

Why would steps need to communicate?

Allowing steps to communicate allows for clearer code, and better code reuse between tests.

This can be explained in the context of an example test from our application's code base; like many of our tests, it mocks up our primary backend service (order services, or OS) with a fixed response from a previously stored file:

(deftest cancel-order
  (let [order-path "os-responses/curbside-pickup-and-fc-delivery.json"
        cid (order-path->cid order-path)]
    (with-test-system [system (new-test-system {:os-client (mocks/mock-os-client
                                                              (tc/read-resource-as-os-payload order-path))})]
      (with-redefs [validate-auth (mock-validate-auth cid)]
        (with-now "2020-08-24T20:00:00.000-0800"
          (let [transaction-id (gen/uuid)
                q "
              mutation($input: CancelOrderInput) {
                cancelOrder(input: $input) {
                   result: cancellationResult {
                variables {:input {:orderId "8508200004824"
                                   :transactionId transaction-id
                                   :subReasonCode "209"
                                   :orderLines [{:lineId "1"
                                                 :quantity 1}]
                                   :cancelAction "CANCEL_NOW"}}
                response (process-request system q variables)]
            (reporting response
                       (is (= 200 (:status response)))
                       (is (= {:data
                                 {:status "SUCCESS"
                                  :statusText "Canceled"
                                  :statusSubText "You canceled this item on Aug 24"
                                  :omsCode nil}}}}
                              (:body response))))))))))

This test is organized around a GraphQL request and response; we have to provide mock components, start and stop a Component system, override the current date/time, send the request into the system, and finally make assertions about the response.

Our premise is that the test is somewhat difficult to understand and maintain; the code reflects how to do the work of the test, rather than what behavior the test is designed to verify.

By contrast, a rewrite of the same test to use test-pipeline (as alias p) is less busy, less deeply nested, and easier to read and maintain:

(deftest cancel-order
    (force-os-response "os-responses/curbside-pickup-and-fc-delivery.json")
    (force-now "2020-08-24T20:00:00.000-0800")
      "mutation($input: CancelOrderInput) {
          cancelOrder(input: $input) {
             result: cancellationResult {
      {:input {:orderId "8508200004824"
               :transactionId (gen/uuid)
               :subReasonCode "209"
               :orderLines [{:lineId "1"
                             :quantity 1}]
               :cancelAction "CANCEL_NOW"}})
    (expect-data {:cancelOrder
                   {:status "SUCCESS"
                    :statusText "Canceled"
                    :statusSubText "You canceled this item on Aug 24"
                    :omsCode nil}}}))

This style of test reads more like a recipe, with more clearly deliniated steps that all work together to accomplish the final result.

Again, most of these functions are specific to our application. default-system is a step function that initializes the base Component system map and places it in the :system key of the context. force-os-response reads the JSON file and mocks the Component responsible for communicating with the external system; it can also mock the authentication function.

Because start-system is an explicit step, it is easy to inject mocks into the system map in whatever order is convenient, prior to the system being started.

send-request sends the request and captures the response as context key :response. expect-success asserts that the response is status 200 and no GraphQL errors are present. expect-data asserts that the :data key of the body matches the provided value.

What we've done is establish a convention for how test data is stored into the context, so that individual steps can read or update that data; thus expect-success knows that a prior step has recorded a :response key into the context, and expect-data can use that same :response key.

Creating Steps

Each step function takes a context map as its only parameter, and then invokes p/continue to continue to the next step. Obviously, a real step function will do something useful first, such as override a component in a component system, or redefine a function with a mock, make an assertion with clojure.test/is, or anything else that's needed.

For example, the start-system step from the above example is coded as:

(defn start-system
  (let [system (-> context :system component/start-system)]
       (p/continue (assoc context :system system)
         (component/stop-system system)))))

This is a function that accepts a context, operates on it, and passes it to the continue function (which, in turn, finds the next step function, and passes the context to that). The try ensures that the system is stopped, regardless of what happens in later steps.

Often, a step requires data specific to a particular test; in that case, a step function factory can create a step function that is passed to p/execute. For example, the expect-data function isn't a step itself; it is a factory that returns a step function:

(defn expect-data
   (fn [context]
     (is (= data (get-in context [:response :body :data])))
     (p/continue context))

Although the step function returned by expect-data is typically the final step in the pipeline, it should still call continue just in case it isn't. For example, a test might use expect-data to assert an expected response, but then have further steps to make other assertions, such as checking how data was persisted to a database.

The execute function will throw an exception if the final step function never gets invoked (due to a prior step not calling continue), as this is almost certainly a bug in the step function implementation.


The pipeline execution can be teminated with halt; halt exists to avoid the above check that all steps executed.

This is useful when an early failure (say, an incorrect HTTP response from a server) will lead to a crowd of meaningless failures further on (such as validating the HTTP response).

The function halt-on-failure is often more useful, it ensures that after any step where test failures or errors occur, the pipeline execution is terminated. Typically, this is often used when initially developing the code and tests, but can be discarded once everything is stable.

What's in the box?

The library itself is quite small; here's the key functions and macros:

  • execute is the primary entrypoint
  • continue is invoked by a step function to continue to the next step
  • mock is used to override a function with a mock implementation
  • spy is used to capture arguments passed to a function, and optionally mock it at the same time
  • calls is used to obtain the captured arguments to a spied function
  • update-in-context and assoc-in-context are used to modify the context during execution
  • capture-logging captures log events; a wrapper around
  • halt terminates pipeline execution
  • halt-on-failure terminates execution if any test failures occur

Please refer to the API documentation for more details.


Copyright © 2022-Present Howard Lewis Ship

Distributed under the Apache License, Version 2.0.

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