How to Setup CI/CD Pipeline for automated API Tests

How to Setup CI/CD Pipeline for automated API Tests

Automating API test suite execution through CI/CD pipelines provides a significant advantage over local execution. By leveraging CI/CD, teams can obtain test results for all systems, improving the speed, quality, and reliability of tests. Manual triggering of API suite execution is not required, freeing up valuable time for team members.

In this blog post, we will guide you through the creation of a workflow file using GitHub Actions for your automated API tests. However, before diving into the creation of a CI/CD workflow, it’s essential to understand some crucial points for a better grasp of the concept.

Before we start creating a CI/CD workflow for our API tests I will suggest you first go through the API test automation framework here and also read this blog on creating a web test automation framework as it helps you to understand the different points which we all should consider before selecting the test automation framework. The API test automation framework is in Python language and has Behave library for BDD purposes.

Let’s understand some basic and important points to start with the CI/CD workflow.

What is DevOps?

DevOps is a set of practices and tools that integrate and automate tasks in the software development and IT industry. It establishes communication and collaboration between development and operations teams, enabling faster and more reliable software build, testing, and release processes. DevOps is a methodology that derives its name from the combination of “Development” and “Operations.”

The primary goal of DevOps is to bridge the gap between development and operations teams by fostering a culture of shared responsibility and collaboration. This helps to reduce the time it takes to develop, test, and deploy software while maintaining high quality and reliability standards. By automating manual processes and eliminating silos between teams, DevOps enables organizations to respond more quickly to changing market demands and customer needs.

To know more about DevOps and its history, please visit the site https://en.wikipedia.org/wiki/DevOps 

CI/CD-1

What is CI/CD?

CI/CD refers to Continuous Integration and Continuous Delivery, which are processes and practices that help to deliver code changes more frequently and reliably. These processes involve automating the building, testing, and deployment of code changes, resulting in faster and higher-quality software releases for end-users.

The CI/CD pipeline follows a workflow that starts with continuous integration (CI), followed by continuous delivery (CD). The CI process involves integrating code changes into a shared repository and automatically building and testing them to identify errors early in the development process. Once the code has been tested and approved, the CD process takes over and automates the delivery of code changes to production environments.

The CI/CD pipeline workflow helps to reduce the risks and delays associated with manual code integration and deployment while ensuring that the changes are tested and delivered quickly and reliably. This approach enables organizations to innovate faster, respond more quickly to market demands, and improve overall software quality.

Process:

CI/CD-2

What are GitHub Actions?

GitHub Actions is a feature that makes it easy to automate software workflows, including world-class CI/CD capabilities. With GitHub Actions, you can build, test, and deploy your code directly from GitHub, while also customizing code reviews, branch management, and issue-triaging workflows to suit your needs.

To learn more about GitHub Actions, please refer to the official documentation available here
https://docs.github.com/en/actions

The GitHub platform offers integration with GitHub Actions, providing flexibility for customizing workflows to automate tasks such as building, testing, and deploying code. Developers can create custom workflows using GitHub Actions that are automatically triggered when specific events occur, such as code push, pull request merge, or as per a defined schedule.

Workflows are defined using YAML syntax, which is a human-readable data serialization language. YAML is commonly used for configuration files and in applications to store or transmit data. To learn more about YAML syntax and its history, please visit the following link

Advantages / Benefits of using GitHub Actions for CI/CD Pipeline:

  • Seamless integration: GitHub Actions seamlessly integrates with GitHub repositories, making it easy to automate workflows and tasks directly from the repository.
  • Highly customizable: GitHub Actions offers a high degree of customization, allowing developers to create workflows that suit their specific needs.
  • Time-saving: GitHub Actions automates many tasks in the software development process, saving developers time and reducing the potential for errors.
  • Flexible: GitHub Actions can be used for a wide range of tasks, including building, testing, and deploying applications.
  • Workflow visualization: GitHub Actions provides a graphical representation of workflows, making it easy for developers to visualize and understand the process.
  • Large community: GitHub Actions has a large and active community, providing a wealth of resources, documentation, and support for developers.
  • Cost Saving: GitHub Actions come bundled with Github free and enterprise licenses reducing the cost of maintaining separate CI/CD tools like Jenkins

Framework Overview:

This is a BDD API automation testing framework. The reason behind choosing the BDD framework is simple it provides you the following benefits over other testing frameworks. 

  • Improved Collaboration
  • Increased Test coverage
  • Better Test Readability
  • Easy Test Maintenance
  • Faster Feedback
  • Integration with Other Tools
  • Focus on Business Requirements

Discover what are the different types of automation testing frameworks available and why to prefer the BDD framework over others here

Framework Explanation:

The framework is simple because we included a feature file written in the Gherkin language, as you will notice. Basically, Gherkin is a simple plain text language with a simple structure. The feature file is easy to understand for a non-technical person and that is why we prefer the BDD framework for automation. To learn more about the Gherkin language please visit the official site here https://cucumber.io/docs/gherkin/reference/. Also, we have included the POST, GET, PUT & DELETE API methods. A feature file describes all these methods using simple and understandable language.

The next component of our framework is the step file. The feature and step files are the two main and most essential parts of the BDD framework. The step file contains the implementation of the steps mentioned in the feature file. It maps the respective steps from the feature file and executes the code.We use the behave library to achieve this. The behave understands the maps of the steps with the feature file steps as both steps have the same language structure. 

Then there is the utility file which contains the methods which we can use more repeatedly. There is one configuration file where we store the commonly used data. Furthermore, to install all the dependencies, we have created a requirement.txt file which contains the packages with specific versions. To install the packages from the requirement.txt file we have the following command. 

pip install -r requirement.txt

The above framework is explained in detail here. I suggest you please check out the blog first and understand the framework then we can move further with the workflow detail description. A proper understanding of the framework is essential to understand how to create the CI/CD workflow file.  

How to create a Workflow File?

  • Create a GitHub repository for your framework
  • Push your framework to that repository
  • Click on the Action Button
  • Click on set workflow your self option
  • Give a proper name to the workflow file

“Additionally, please check out the below video for a detailed step understanding.” The video will show you how to create workflow files and the steps need to follow to do so. 

github actions workflow file creation

Components of CI/CD Workflow File:

Events:

Events are responsible to trigger the CI/CD workflow file. They are nothing but the actions that happen in the repository for example pushing to the branch or creating a pull request. Please check the below sample events that trigger the CI/CD workflow file. 

  • push: This event is triggered when someone pushes code to a branch in your repository.
  • pull_request: This event is triggered when someone opens a new pull request or updates an existing one.
  • schedule: This event is triggered on a schedule that you define in your workflow configuration file.
  • workflow_dispatch: This event allows you to manually trigger a workflow by clicking a button in the GitHub UI.
  • release: This event is triggered when a new release is created in your repository.
  • repository_dispatch: This event allows you to trigger a workflow using a custom webhook event.
  • page_build: This event is triggered when GitHub Pages are built or rebuilt.
  • issue_comment: This event is triggered when someone comments on an issue in your repository.
  • pull_request_review: This event is triggered when someone reviews a pull request in your repository.
  • push_tag: This event is triggered when someone pushes a tag to your repository.

To know more about the events that trigger workflows please check out the GitHub official documentation here

Jobs:

After setting up the events to trigger the workflow the next step is to set up the job for the workflow. The job consists of a set of steps that performs specific tasks. For every job, there is a separate runner or we can call it a virtual machine (VM) therefore each job can run parallelly. This allows us to execute multiple tasks concurrently. 

A workflow can have more than one job with a unique name and set of steps that define the actions to perform. For example, we can use a job in the workflow file to build the project, test its functionality, and deploy it to a server. The defined jobs in the workflow file can be dependent on each other. Also, they can have different requirements than the others like specific operating systems, software dependencies or packages, or environment variables. 

Discover more about using jobs in a workflow from GitHub’s official documentation here

Runners:

To execute the jobs we need runners. The runners in GitHub actions are nothing but virtual machines or physical servers. GitHub categorizes them into two parts named self-hosted or provided by GitHub. Moreover, the runners are responsible for running the steps described in the job.

The self-hosed runners allow us to execute the jobs on our own system or infrastructure for example our own physical servers, virtual machines, or containers. We use self-hosted runners when we need to run jobs on specialized hardware requirements that must be met.

GitHub-hosted runners are provided by GitHub itself and can be used for free by anyone. These runners are available in a variety of configurations. Furthermore, the best thing about GitHub-hosted runners is that they automatically update with the latest software updates and security patches.

Learn more about runners for GitHub actions workflow here from GitHub’s official documentation. 

Steps:

Steps in the workflow file are used to carry out particular actions. Subsequently, after adding the runner to the workflow file, we define these steps with the help of the steps property in the workflow file. Additionally, the steps consist of actions and commands to perform on the build. For example, there are steps to download the dependencies, check out the build, run the test, upload the artifacts, etc. 

Learn more about the steps used in the workflow file from GitHub’s official documentation here

Actions:

In the GitHub actions workflow file, we use actions that are reusable code modules that can be shared across different workflows and repositories. One or more steps are defined under actions to perform specific tasks such as running tests, building the project, or deploying the code. We can also define the input and output parameters to the actions which help us to receive and return the data from other steps in the workflow. Developers describe the actions, and they are available on GitHub Marketplace. To use an action in the workflow, we need to use the uses property.

Find out more about actions for GitHub actions from GitHub’s official documentation here 

Now we have covered all the basic topics that we need to understand before creating our CI/CD workflow file for the API automation framework. Now, let’s start explaining the workflow file.

CI/CD Workflow File:

name: Python API CI/CD Pipeline
on:
  push:
   branches: ["main"]
#    schedule:
#       - cron: '00 12 * * *'
jobs:
 build:
  runs-on: windows-latest
  steps:
    - uses: actions/checkout@v3
    - name: Set up Python
      uses: actions/setup-python@v3
      with:
        python-version: '3.8.9'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip && pip install -r requirement.txt
    - name: install allure
      run:  npm install -g allure-commandline
      continue-on-error: true
    - name: run test
      run: behave Features -f allure_behave.formatter:AllureFormatter -o Report_Json
      working-directory: .
      continue-on-error: true
    - name: html report
      run: allure generate Report_Json -o Report_Html --clean
      continue-on-error: true
    - uses: actions/upload-artifact@v2
      with:
          name: HTML reports
          path: Report_Html
      continue-on-error: true

Explanation:

Name:

  • We use the name property to give the name to the workflow file. It is a good practice to give a proper name to your workflow file. Generally, the name is related to the feature or the repository name. 
name: Python API CI/CD Pipeline

Event:

Now we have to set up the event that triggers the workflow file. In this workflow, I have added two events for your reference. The pipeline will trigger the push event for the ‘main‘ branch. Additionally, I added the scheduled event to automatically trigger the workflow as per the set schedule.

on:
   push:
    branches: ["main"]
#    schedule:
#       - cron: '00 12 * * *'

The above schedule indicates that the pipeline Runs at 12:00. Action schedules run at most every 5 minutes using UTC time.

We can customize the schedule timing as per our needs. Check out the following chron specification diagram to learn how to set the schedule timing.

Job:

The job we are setting here is to build. We want to build the project and perform the required tasks as we merge new code changes.

jobs:
  build:

Runner:

The runner we are using here is a GitHub-hosted runner. In this workflow, we are using a Windows-latest virtual machine. The VM will build the project, and then it will execute the defined steps.

runs-on: windows-latest

Apart from Windows-latest, there are other runners too like ubuntu-latest, macos-latest, and self-hosted. The self-hosted runner is one that we can set up on our own infrastructure, such as our own server, or virtual machine, allowing us to have more control over the environment and resources.

Steps:

The steps are the description of what are the different actions required to perform on the project build. Here, the first action we are performing is to check out the repository so that it can have the latest build code. 

steps:
- uses: actions/checkout@v3

Then we are setting up the Python. As this framework is an API automation testing framework using Python and Behave so we need Python to execute the tests. 

- name: Set up Python
  uses: actions/setup-python@v3
      with:
         python-version: '3.8.9'

After we install Python, we also need to install the different packages required to run the API tests. Define these packages in the requirement.txt file, and we can install them using the following command.

- name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip && pip install -r requirement.txt

For reporting purposes, we are using allure reports. To generate the allure report we need to install the allure package separately.

- name: install allure
  run:  npm install -g allure-commandline
  continue-on-error: true

As of now, we have installed all the packages and we can now run our API tests. We are running these tests with the help of the allure behave command so that once the execution is completed it will generate a Report_Json folder which is required to generate the HTML report. 

- name: run test
 run: behave Features -f allure_behave.formatter:AllureFormatter -o Report_Json
 working-directory: .
 continue-on-error: true

Here, we cannot share the generated Report_Json folder as a report. To generate the shareable report we need to convert the JSON folder to that of the HTML report. 

- name: html report
run: allure generate Report_Json -o Report_Html --clean
continue-on-error: true

To view the report locally we need to upload the artifacts first and then only we can download the generated HTML result. 

- uses: actions/upload-artifact@v2
     with:
          name: HTML reports
          path: Report_Html
          continue-on-error: true

How to download and view the HTML Report?

Please find the attached GitHub repository link. I have uploaded the same project to this repository and also attached a Readme file that explains the framework and the different commands we have used so far in this project. Also, the workflow explanation is included for better understanding.

Conclusion:

In conclusion, creating a CI/CD pipeline workflow for your project using GitHub Actions streamlines the development and testing process by automating tasks such as building the project for new changes, testing the build, and deploying the code. This results in reduced time and minimized errors, ensuring that your software performance is at its best.

GitHub Actions provides a wide range of pre-built actions and the ability to create custom actions that suit your requirements. By following established practices and continuously iterating on workflows, you can ensure your software delivery is optimized and reliable.

I hope in this blog I have provided the answers to the most commonly asked question and I hope this will help you to start creating your CI/CD pipelines for your projects. Do check out the blogs on how to create a BDD framework for Web Automation and API automation for a better understanding of automation frameworks and how a robust framework can be created. 

API BDD Test automation framework using Behave and Python

API BDD Test automation framework using Behave and Python

API’s the term we heard a lot and wanted to know more about it. The questions that come to our mind are what is it? Why is it so important? How to test it? So, let’s just explore these questions one by one. API testing is accessible only if you know what to test and how to test. Again, a proper framework will help you to achieve your goals and deliver a good quality of work. The importance of automation framework and the factors we should consider for choosing the proper framework are described in our previous blog. Please go through the blog here, then you can start reading this blog because you will have a good understanding of automation testing frameworks. 

To build the API testing framework we will be using the BDD approach. Again, why I have chosen a BDD framework for API testing the reason is very simple the BDD approach provides you with an easy understanding of the framework, you can easily maintain the framework and they have feature files that are very easy to understand for a non-technical person. 

What is API?

API (Application Programming Interface) is like a mechanism that works between the two software components and helps them to communicate with each other. The communication happened using sets of definitions and set protocols. In simple language, API works as an intermediate between two systems and helps them exchange data and communicate. The working mechanism of Rest API is straightforward they work by sending requests and receiving a response in a standardized format. Usually, the standardized format used for Rest API is JSON i.e. (JavaScript Object Notation) 

Let’s understand it better with an example. Consider you are using a ticket booking app to book a flight ticket. As the app is connected to the internet so it will set data to the server. When the server receives the data it interprets it and performs the required actions and sends it back to your device. Then the application translates that data and display the information in a readable way. So this is how API works. I hope you have understood the working mechanism of API’s now let’s discuss the next topic i.e. 

What is API Testing?

As we have understood what is an API and how they work so let’s see why their testing is important. Basically, API testing is a part of software testing that includes the testing of the functionality, reliability, security, and performance of API. API is used for data transfer and to establish communication between the two systems so testing APIs includes verifying that the APIs are meeting its requirement, performing as per the expectations, and can handle a variety of inputs. This testing provides you the information that the API’s functionality is correct and efficient and the data they return is accurate and consistent. 

Why is API Testing Important?

API testing is an important part of a Software testing process as it helps you to understand the functionality of the working APIs and validate any defect present before the application is released to the end users. The other key reasons why API testing is important to include: 

  • Ensuring Functionality
  • Validating data integrity
  • Enhancing the Security
  • Improving the Performance
  • Detecting Bugs and Issues
  • Improving readability and stability
  • Facilitating integration and collaboration 

All the above-mentioned points get checked and validate in API testing. Till now we have discussed what is api, what is api testing, and why it is important. Let’s see what different tools are available to conduct the manual as well as automation testing of API. 

Tools for Manual API Testing:

  1. Postman
  2. SoapUI
  3. Insomnia
  4. Paw
  5. Advanced REST Client (ARC)
  6. Fiddler
  7. cURL

Tools for API Automation Testing:

  1. Postman
  2. SoapUI
  3. RestAssured
  4. RestSharp
  5. Apache HTTP client
  6. JMeter
  7. Karate
  8. Newman
  9. Pact.js
  10. Cypress.js

These are just a few examples of the tools available for both manuals as well as automation testing of API. Each mentioned tool has its own strength and weakness and the choice of the right tool for your API testing depends upon the requirement and the specific needs of the project. These tools will help us to ensure that the APIs meet the desired functionality and performance requirements. 

Now we are more familiar with APIs so let’s start the main topic of our discussion and i.e. Python Behave API Testing BDD Framework. 

Framework Overview:

To validate all the above-mentioned points creating a robust API testing framework is very essential. With the help of the below-mentioned steps, you will come to know how to create your own API testing framework. Here, we are going to create a BDD framework. Please go through this blog before starting to read this blog as the previous blog will help you to understand the advantages of BDD and this blog is linked to the previous blog topics. You can read the previous blog here

This framework structure contains a feature file, a step file, and a utility file. We will be discussing all these terms shortly. To create such a framework you need to follow certain steps to make your work tedious-free and easy. 

Step1: Prerequisites

  1. Python: https://www.python.org/downloads/ visit the site to download and install python in your system if it is not there.
  2. Pycharm IDE (Professional or Community): https://www.jetbrains.com/pycharm/download/ 
  3. Install all the required packages using the below command as long as you have all the packages mentioned in rquirement.txt with the right version number

pip install -r requirement.txt

  1. To know more about behave, allure report please visit https://pypi.org/project/behave/ & https://pypi.org/project/allure-behave/
  2. We can also install the mentioned packages from the settings of Pycharm IDE 

Step2: Creating Project

After understanding the prerequisites the next step is to create a project in our IDE. Here I am using a Pycharm Professional IDE. As mentioned in the above step, we will install the packages mentioned in the requirement.txt file. Please note it is not compulsory to use Pycharm Professional IDE to create this framework you can use the community version too. 

Step3: Creating a Feature File

In this, we will be creating a feature file. A feature file consists of steps. These steps are mentioned in the gherkin language. The feature is easy to understand and can be written in the English language so that a non-technical person can understand the flow of the test scenario. In this framework we will be automating the four basic API request methods i.e. POST, PUT, GET and DELETE.  We are taking https://reqres.in/

We can assign tags to our scenarios mentioned in the feature file to run particular test scenarios based on the requirement. The key point you must notice here is the feature file should end with .feature extension. We will be creating four different scenarios for the four different API methods. 

Feature: User API
Verify the GET PUT POST DELETE methods of User API
  @api
  Scenario: Verify GET call for single user
    When User sends "GET" call to endpoint "api/users/2"
    Then User verifies the status code is "200"
    And User verifies GET response contains following information
      | First_name | Last_name | Mail-id                |
      | Janet      | Weaver    | janet.weaver@reqres.in |

  @api
  Scenario: Verify POST call for single user
    When User sends "POST" call to endpoint "api/users"
      | Name   | Job  |
      | Yogesh | SDET |
    Then User verifies the status code is "201"
    And User verifies POST response body contains following information
      | Name   | Job  |
      | Yogesh | SDET |

  @api
  Scenario: Verify PUT call for single user
    When User sends "PUT" call to endpoint "api/users/2"
      | Name   | Job  |
      | Yogesh | SDET |
    Then User verifies the status code is "200"
    And User verifies PUT response body contains following information
      | Name   | Job  |
      | Yogesh | SDET |

  @api
  Scenario: Verify DELETE call for single user
    When User sends DELETE call to the endpoint "api/users/2"
    Then User verifies the status code is "200"

Step4: Creating a Step File

Unlike the automation framework which we have built in the previous blog, we will be creating a single-step file for all the feature files. In the BDD framework, the step files are used to map and implement the steps described in the feature file. Python’s behave library is very accurate to map the steps with the steps described in the feature file. We will be describing the same steps in the step file as they have described in the feature file so that behave will come to know the step implementation for the particular steps present in the feature file. 

from behave import *
from Utility.API_Utility import API_Utility
api_util = API_Utility()

@when('User sends "{method}" call to endpoint "{endpoint}"')
def step_impl(context, method, endpoint):
    global response
    response = api_util.Method_Call(context.table, method, endpoint)
@then('User verifies the status code is "{status_code}"')
def step_impl(context, status_code):
    actual_status_code = response.status_code
    assert actual_status_code == int(status_code)

@step("User verifies GET response contains following information")
def step_impl(context):
    api_util.Verify_GET(context.table)
    response_body = response.json()
    assert response_body['data']['first_name'] == context.table[0][0]
    assert response_body['data']['last_name'] == context.table[0][1]
    assert response_body['data']['email'] == context.table[0][2]

@step("User verifies POST response body contains following information")
def step_impl(context):
    api_util.Verify_POST(context.table)
    response_body = response.json()
    assert response_body['name'] == context.table[0][0]
    assert response_body['job'] == context.table[0][1]

@step("User verifies PUT response body contains following information")
def step_impl(context):
    api_util.Verify_PUT(context.table)
    response_body = response.json()
    assert response_body['Name'] == context.table[0][0]
    assert response_body['Job'] == context.table[0][1]

@when('User sends DELETE call to the endpoint "{endpoint}"')
def step_impl(context, endpoint):
    api_util.Delete_Call(endpoint)

Step5: Creating Utility File

Till now we have successfully created a feature file and a step file now in this step we will be creating a utility file. Generally, in Web automation, we have page files that contain the locators and the actions to perform on the web elements but in this framework, we will be creating a single utility file just like the step file. The utility file contains the API methods and the endpoints to perform the specific action like, POST, PUT, GET, or DELETE. The request body i.e. payload and the response body will be captured using the methods present in the utility file. So the reason these methods are created in the utility file is that we can use them multiple times and don’t have to create the same method over and over again. 

import json
import requests
class API_Utility:
    data = json.load(open("Resources/config.json"))
    api_url = data["APIURL"]
    global response

    def Method_Call(self, table, method, endpoint):
        if method == 'GET':
            uri = self.api_url + endpoint
            response = requests.request("GET", uri)
            return response

        if method == 'POST':
            uri = self.api_url + endpoint
            payload = {
                "name": table[0][0],
                "job": table[0][1]
            }
            response = requests.request("POST", uri, data=payload)
            return response

        if method == 'PUT':
            uri = self.api_url + endpoint
            reqbody = {
                "Name": table[0][0],
                "Job": table[0][1]
            }
            response = requests.request("PUT", uri, data=reqbody)
            return response

    def Get_Status_Code(self):
        status_code = response.status_code
        return status_code

    def Verify_GET(self, table):
        for row in table:
            first_name = row['First_name']
            last_name = row['Last_name']
            email = row['Mail-id']
            return first_name, last_name, email

    def Verify_POST(self, table):
        for row in table:
            name = row['Name']
            job = row['Job']
            return name, job

#Following method can be merged with POST, however for simplicity I kept it
    def Verify_PUT(self, table):
        for row in table:
            name = row['Name']
            job = row['Job']
            return name, job

    def Delete_Call(self, endpoint):
        uri = self.api_url + endpoint
        response = requests.request("DELETE", uri)
        return response

Step6: Create a Config file

A good tester is one who knows the use and importance of config files. In this framework, we are also going to use the config file. Here, we are just going to put the base URL in this config file and will be using the same in the utility file over and over again. The config file contains more data than just of base URL when you start exploring the framework and start automating the new endpoints then at some point, you will realize that some data can be added to the config file. 

Additionally, the purpose of the config files is to make tests more maintainable and reusable. Another benefit of a config file is that it makes the code more modular and easier to understand as all the configuration settings are stored in a separate file and it makes it easier to update the configuration settings for all the tests at once. 


     "APIURL": "https://reqres.in/"

Step7: Execute and Generate Allure Report

The reason behind using allure reports as a reporting medium is because the allure report provides detailed information about the test execution process and results which includes the test status, test steps, duration, and screenshots of the test run. The report is generated in HTML i.e. web format making it easy to share with team members and with clients and easy to understand. It provides a dashboard that is user-friendly having interactive charts and graphs that provide a detailed analysis of the test results. 

Let’s understand how to execute API tests and generate an allure report for automated API calls. To generate the report we will have to execute the test using the terminal or command line. There are two steps to follow sequentially they are as follows:

  1. behave Features/Api.feature -f allure_behave.formatter:AllureFormatter -o Report_Json

The purpose of the above command is to execute the test present in the mentioned feature file and generate a JSON report folder. 

  1. allure generate Report_Json -o Report_Html –clean

This command is used to generate an HTML report from the JSON report. So, that it is easy to understand and can be shared with team members or clients. 

Please find the attached GitHub repository link. I have uploaded the same project to this repository and also attached a Readme.md file which explains the framework and the different commands we have used so far in this project. 

https://github.com/spurqlabs/PythonBehaveApiFramework

Conclusion:

Before creating a framework it is very important to understand the concept and I hope I have provided enough information for the different queries on APIs. In conclusion, creating a BDD API testing framework using Python and Behave is easy to process if you know how to proceed further. By following the steps outlined in this blog I am sure you can create a powerful and flexible framework that will help you to define and execute the test cases, generate a detailed report with allure and also iterate with other testing tools and systems.  Again I am suggesting you check out the previous blog here because that will clear most of your doubts on automation testing frameworks and will help you to create your own automation testing framework. 

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API Test Automation using Playwright and Java

API Test Automation using Playwright and Java

 Most of us are familiar with API testing tools like Postman, SoapUI, etc, and API automation libraries like RestAssured and Karate to automate API test cases. A recent entrant in this category is Playwright. The playwright is an Automation Tool provided by Microsoft. It provides cross-browser testing using which we can perform testing on multiple browsers like Chromium, Webkit, Firefox, etc. playwright supports cross-language testing (Java, Node.js, Python, .NET, etc.). However, very few of us know that it can actually do an API Test automation of methods GET, PUT, POST, and DELETE. Let’s see how it can be done.

Can we perform API testing using Playwright?

The playwright provides inbuilt support for API testing that’s why we don’t need any external libraries to handle API. The playwright doesn’t use any browser to call API. It sends requests using Node.js which provides faster execution.

In this tutorial, we will explore basic API methods with the help of Playwright- java. Below are the Four methods.

  1. GET
  2. POST
  3. PUT
  4. DELETE

Pre-requisite:

To get started with API automation with playwright-java first we need playwright to be installed in your system, to do this we can simply add the following dependency in the pom.xml file.

Along with the playwright, we have to add Testing and JSON dependencies.

<dependency>
  <groupId>com.microsoft.playwright</groupId>
  <artifactId>playwright</artifactId>
  <version>1.23.0</version>
</dependency>

<dependency>
   <groupId>org.testng</groupId>
   <artifactId>testng</artifactId>
   <version>7.1.0</version>
   <scope>test</scope>
</dependency>

<dependency>
   <groupId>com.googlecode.json-simple</groupId>
   <artifactId>json-simple</artifactId>
   <version>1.1.1</version>
</dependency>

Now let’s see how we can start API automation testing with Playwright-java.

1. GET:

By providing an API endpoint we can read data using a GET request. We must pass a few parameters to get the required data from the API.

We can verify the request by asserting the Response Code. The response code for a successful GET Request is 200. You can also assert a text inside the JSON body response.          

For Example, I am using postman here to send a GET request to ”  ‘/api/users/4’ endpoint of a sample API URL ‘https://reqres.in’

The below code shows the implementation of the GET method through Playwright.

@Test
public void get() throws IOException, ParseException {
   Playwright playwright = Playwright.create();
   String BaseURL="https://reqres.in";
   Map<String, String> headers = new HashMap<>();
   headers.put("content-type", "application/json");
   APIRequestContext apiRequestContext= playwright.request().newContext(new APIRequest.NewContextOptions()
           .setBaseURL(BaseURL).setExtraHTTPHeaders(headers));
   APIResponse response = apiRequestContext.get("/api/users/4");
   int responseCode=response.status();
   Assert.assertEquals(responseCode,200);

   String responseText= response.text();
   JSONParser j= new JSONParser();
   JSONObject json = (JSONObject) j.parse(responseText);

   String responseData= json.get("data").toString();
   JSONObject jsonData = (JSONObject) j.parse(responseData);
   Assert.assertEquals(jsonData.get("email"),"eve.holt@reqres.in");
   Assert.assertEquals(jsonData.get("first_name"), "Eve");
   Assert.assertEquals(jsonData.get("last_name"), "Holt");
   Assert.assertEquals(jsonData.get("avatar"), "https://reqres.in/img/faces/4-image.jpg");
   apiRequestContext.dispose();
   playwright.close();
}

To verify the response data we are parsing the response to JSON Object so that we can verify specific key-value pairs.

2. POST:

POST method is used to add a new record via API where we have to pass data as payload ex. first name, last name, etc. The response code for a successful POST Request is 201.

For example, I am sending a POST request to  ‘/api/users/’ endpoint with base URL ‘https://reqres.in‘ and the body as given below in the screenshot:

To pass the data payloads to the POST/PUT method, first, we have to create a POJO class which will help to create methods to get and set payloads.
below we have created a POJO class employee which will help to get and set data to both POST and PUT calls.

public class Employee {
// private variables or data members of pojo class
private String email;
private String first_name;
private String last_name;
private String avatar;

public String getEmail() {
   return email;
}
public void setEmail(String email) {
   this.email = email;
}
public String getFirstName() {
   return first_name;
}
public void setFirstName(String firstName) {
   this.first_name = firstName;
}
public String getLastName() {
   return last_name;
}
public void setLastName(String lastName) {
   this.last_name = lastName;
}
public String getAvatar() {
   return avatar;
}
public void setAvatar(String avatar) {
   this.avatar = avatar;
}
}

The below code will help you with the POST method through Playwright.

@Test
public void post() throws ParseException {
   employee employee = new employee();
   employee.setEmail("john@reqres.in");
   employee.setFirstName("john");
   employee.setLastName("Holt");
   employee.setAvatar("https://reqres.in/img/faces/4-image.jpg");

   Playwright playwright = Playwright.create();
   String BaseURL = "https://reqres.in";
   Map<String, String> headers = new HashMap<>();
   headers.put("content-type", "application/json");
   APIRequestContext apiRequestContext = playwright.request().newContext(new APIRequest.NewContextOptions()
           .setBaseURL(BaseURL).setExtraHTTPHeaders(headers));
   APIResponse response = apiRequestContext.post("/api/users/", RequestOptions.create().setData(employee));

   Assert.assertEquals(response.status(),201);
   String responseText= response.text();
   JSONParser j= new JSONParser();
   JSONObject json = (JSONObject) j.parse(responseText);
   Assert.assertEquals(json.get("email"), employee.getEmail());
   Assert.assertEquals(json.get("first_name"), employee.getFirstName());
   Assert.assertEquals(json.get("last_name"), employee.getLastName());
   Assert.assertEquals(json.get("avatar"), employee.getAvatar());
   apiRequestContext.dispose();
   playwright.close();
}

3. PUT:

PUT Request is used to update the existing records via the API. we have to pass the data we want to update as a payload ex. first name, last name, etc. The response code for a successful PUT Request is 200.

For example, I am sending a POST request to ‘/api/users/55’ endpoint with the base URL ‘https://reqres.in’ and the body as given below in the screenshot:

The below example shows the implementation of the PUT method. We need to use the above POJO class to pass the data as a payload to the PUT call.

 @Test
public void put() throws ParseException {
   employee employee = new employee();
   employee.setEmail("john@reqres.in");
   employee.setFirstName("Harry");
   employee.setLastName("Eve");
   employee.setAvatar("https://reqres.in/img/faces/4-image.jpg");

   Playwright playwright = Playwright.create();
   String BaseURL = "https://reqres.in";
   Map<String, String> headers = new HashMap<>();
   headers.put("content-type", "application/json");
   APIRequestContext apiRequestContext = playwright.request().newContext(new APIRequest.NewContextOptions()
           .setBaseURL(BaseURL).setExtraHTTPHeaders(headers));
   APIResponse response = apiRequestContext.put("/api/users/55", RequestOptions.create().setData(employee));

   Assert.assertEquals(response.status(),200);
   String responseText= response.text();
   JSONParser j= new JSONParser();
   JSONObject json = (JSONObject) j.parse(responseText);
   Assert.assertEquals(json.get("email"), employee.getEmail());
   Assert.assertEquals(json.get("first_name"), employee.getFirstName());
   Assert.assertEquals(json.get("last_name"), employee.getLastName());
   Assert.assertEquals(json.get("avatar"), employee.getAvatar());
}

4 DELETE:

We can delete existing records using the API by using DELETE Request. Ideally, you must have added a record before you delete it. Hence you would need to append an ID to the DELETE URL. To delete the record using API first we need to pass the record URI (Universal Resource Identifier). The response code for a successful DELETE Request is 200.

For example, I am sending a POST request to ‘https://retoolapi.dev’ endpoint with base URL ‘/3njSPM/calc/43’ and the body as given below in the screenshot:

Following is the code for the DELETE method in Playwright

@Test
public void delete()  {
   Playwright playwright = Playwright.create();
   String BaseURL = "https://retoolapi.dev";
   APIRequestContext apiRequestContext = playwright.request().newContext(new APIRequest.NewContextOptions()
           .setBaseURL(BaseURL));
   APIResponse response = apiRequestContext.delete("/3njSPM/calc/40");
   int responseCode = response.status();
   Assert.assertEquals(responseCode, 200);
   Assert.assertEquals(response.statusText(), "OK");
   apiRequestContext.dispose();
   playwright.close();

 

After performing a DELETE call we can perform a GET call on the same endpoint to verify data is actually deleted. For this GET call, we will get response code 204 as the content is not found.

For Reference: https://playwright.dev/java/docs/api-testing

Conclusion:-

GET, PUT, POST, and DELETE are the basic CRUD API methods used in any Web application. With the help of the inbuilt functionalities of Playwright, API Automation Testing became much easier and faster.

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