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Another layer of testing that we might consider adding to our pipeline, our API tests, API testing

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is a type of testing that ensures that the API exposed by an application is working properly.

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Our application exposes this API.

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It can see here, for example, and point cars will give back this information in an adjacent format

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with the list of cars and also provides other endpoints to add a car, to get a single car, to delete

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a car, toaster to get some statistic information and so on at the high level.

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This is the functionality that this application provides doesn't have a graphical interface.

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It only provides this API from a testing point of view.

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We need to make sure that API is actually working properly.

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Otherwise, we are not sure if the application works or not, because just based on the unit test,

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for example, we cannot tell if the application actually works, if these endpoints provide this information,

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since we have used Bowsman in order to interact with API, can also use Posman to write some very,

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very simple API tests.

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And even if you are not familiar with Posman, I'm sure you'll be able to follow along.

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You will get the collection with all the tests as a resource from this lecture and you can look at it

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and play around with it.

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But let me show you a very simple example with a test.

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In this case, I'm getting all the cars and the fundamental thing that we can test in this situation

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is if the status was 200, for example, if I'm just calling something like a car, I'll get a 404 not

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found because this page is doesn't exist.

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This API address and point doesn't exist for the cars.

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I'm getting at 200.

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OK, I'm going here to the test.

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Deb, we can look at existing snippets that you can open from the right side here in case they are not

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opened and we can test for something like status is 200.

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What is does is it's this piece of code here, which is actually a test, very simple test.

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This will ensure that the status code is 200.

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Guess you want to learn more about API testing with Bowsman, all attach a resource to a video which

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introduces you to both men and API testing overall.

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What is an API and how to get started?

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But not to keep this section as short and as focused as possible, just going to go into the absolute

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basics.

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We just kind of focus on the fundamentals and not go into many details.

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I'm going to save the tests and what I can do then is to go over this collection that I have here and

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to export it.

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Now to select the latest version that is available and hit export.

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Navigated a project path and saved the collection inside the folder of the project.

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If you go back to intelligence, you'll be able to see here the Cars API Postmen Collection may also

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decide to create another folder within the project and to store their multiple collections or however

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you decide to organize your code.

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In order to actually execute the tests that are in the collection, we need to use the CIA to implement

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itself, does not have a CLIA tool, so we need to use a companion tool that comes from the postmen

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project.

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It's called Newman.

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And you can use Newman from UCLA.

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Now, I already have a Docker image that has new one installed so we can just jump and use that Docker

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image without worrying too much about it.

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But just for you to know what is Neumont and what's the relationship with oarsmen?

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So let's go ahead and add the stage of API test.

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Now, the idea would be to test an application that has already been deployed to a server in this case,

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the only server that we have is actually the production server.

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This kind of test will not warn us in advance.

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If there's something wrong with our application, it will warn us when it is a bit too late.

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But it still important sometimes to have post deployment tests just to ensure that the application is

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working properly.

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And because we do not have any environment in between.

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We do not have a testing or a preproduction environment.

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We're going to run this test after a production server has been deployed.

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But ideally we would run such tests before we deploy to production on a different environment.

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With that being said, let's add another stage.

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I'm going to call the state police deployment.

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For the play that we know that this happens after the deploy and then we can end a new job, API testing.

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These will be passed by.

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And we're going to use an image that has been created by me and it also contains some additional reports

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that are very useful when using human and Posman death.

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This is why the name is the Dasbach.

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Slash Newman additionally going to specify an empty entry point.

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When it comes to the script itself, that's quite easy to execute, the first idea would be to always

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run Newman.

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There's desperation and what this will do, it will print in the console.

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What is the current version of Newman?

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This observation is valid for any tools that we use.

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It's always a good idea to put in the console the version that we are using, not necessarily because

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it provides value right now, just in case all of the sudden something stops working.

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It would know exactly which version has been used because in this case we have not specified a fixed

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version of this Docker image.

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And you can see that we haven't done that for any of the images that we have used, which means that

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if the auteur or if the image itself changes the version of the tools that you are using from a pipeline

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to the other also change.

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Typically, this will not cause any issues because most of the time these tools are pretty stable and

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there are no major changes just in case something changes, you at least would be able to tell.

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Nothing is wrong with my pipeline itself or with the test that I'm running.

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There's something that has changed in the tool so makes it easier to compare the previous pipeline that

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worked used human conversion, I don't know, let's say six.

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And now in your pipeline uses Newman in version seven, which is obviously a difference.

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It's called to the Newman GitHub repository.

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And check if there are any issues or anybody else having the same problem.

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The next step is quite easy.

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Going to simply use the Newman run.

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And then we have to specify the name of the collection because it contains a space.

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It is always a good idea to put it between double quotes so that it's clear which is the path to this

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collection and nothing comes in between or somehow generates any errors as it stands currently.

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It will not work because we have used two different things in Posman.

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We have used the collection itself, but we have also used environments and we need to specify here

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the environment.

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Otherwise that will not work.

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So going back to Bowsman, I'm going to click here on Manege Environments.

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And as I did with a collection, I can export to the environment as well as a file.

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So I'm going to export the production environment this time.

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And just as previously, I'm going to put it in the root of the project.

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And it's always a good idea to just copy the name because we're going to use it right away.

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Now, in this Seelie command, we are also going to specify the environment that will be dishdasha,

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environment.

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And then the environment name not using double quotes, because there are no spaces here, so there's

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no chance of something going wrong.

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Finally, I'm going to specify to reporters.

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And this will be Thalys and the second report there is extra.

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The CNN reporter will ensure that if you look at the console from this job, everything will be written

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down there as well during the e-mail extra report will generate, well, an example, a report that

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can help us understand what's going on with a request.

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So because we are generating an excellent report, we're going to add an additional parameter and we

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misconfiguration, we are going to tell where to export this fire and we want to put it inside the Neumann

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folder.

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In the name of the report should be report that e-mail.

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An additional reporter that we will use is also Janet.

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For Jean, again, we're going to specify a path where this report should be saved would be pretty similar

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to what we have used for the human report just with a different file extension.

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The report would be instead of simply sent out.

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Finally, we need to tell Gittel as well about this reports that we have generated here.

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And the way this works is pretty similar to what we have used before thought.

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And you've a copy from unit test the artifacts and quickly adapted.

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The rule will stay the same, we always generate this artifact and save them.

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It will be inside the Neumann folder, so there's nothing else that we need to add to there.

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And additionally, we are generating this GAO unit report and we know the path to the report.

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This will be quite easy.

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So this will be Newman's last report that them out.

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It seems that we have everything in place for API testing on the production system that saves this configuration.

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Run the pipeline and see how everything looks like.

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Now, the pipeline includes an additional stage post deployment and also API testing that is done on

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the server because we have specified the J unit report format that has been picked up by catalepsy.

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You can see to here under test.

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And now apart from unit tests, we also have API testing.

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You will see here.

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That's only one test that we have because we have only generated one test.

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But this still shows up here and can be scaled without any issues.

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Additionally, going back to the pipeline, we can see the API testing and we also have the CLIA report

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here where we see that there was one assertion, this is a test that we have written.

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It was successful.

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Nothing has failed.

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All the request work without any issues.

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And this is the command that we have executed.

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This is the Newman version.

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Everything works fine from this point of view.

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Additionally, what we can do is to look at the job artifacts here.

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We will be able to find each team and report as well.

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And this is how the report looks like, it contains a lot of information regarding what has been executed,

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what are the total request?

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It's a very nice report to have saved in the run of the pipeline.