In this digital era, in a little span of time, new functionalities and features are added to software technology. Therefore, testers and developers cannot provide their maximum time on a single software application. Thus, they need to increase their development velocity in order to cope with the advancing world, and Playwright test analytics can be used for the same. https://itphobia.com/playwright-test-analytics-extracting-insights-for-improved-developer-velocity/

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Playwright Test Analytics: Extracting
Insights for Improved Developer Velocity
by Shuvo A. | 0 comments
In this digital era, in a little span of time, new functionalities and features
are added to software technology. Therefore, testers and developers
cannot provide their maximum time on a single software application.
Thus, they need to increase their development velocity in order to cope
with the advancing world, and Playwright test analytics can be used for the
same.
A playwright is a powerful approach used by testers and developers to
improve development velocity. It is an open-source Node.js library
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framework used to automate the testing process on various browsers
effectively and efficiently.
Playwright test analytics include systematic collection and analysis of test
data which can further be visualized using any observability platforms
such as Grafana or Kibana to create a clear test report which includes pass
or fail status along with execution time and custom metrics.
Playwright test analytics provides its user with continuous feedback along
with regular analysis of software applications which help them to make
fast decisions, understand the priority of various test case execution, and
address various performance issues quickly, which reduces the overall
testing time, thus increasing the development velocity.
In this article, we will get to know about the benefits of the playwright test
analytics approach, along with how it can be used to improve developer
velocity.
Significance Of Test Analytics
Test Analytics not only collects, analyzes, and interprets data to state
whether the test case passes or fails, but it also helps in developing an
effective and efficient software application due to its following significance:
Identifying Bottlenecks: Test analytics can easily identify bottlenecks,
such as issues with the test environment or test data that causes slow
execution, and then solve them and optimize test execution.
Improving Test Coverage: Test analytics helps testers and developers
to know about the issues present in the software application so that
they can easily update their test cases to test critical functions.
Reducing Test Maintenance: Test analytics can easily identify test
cases that frequently fail so that testers and developers can understand
the root cause of failure, thus saving both time and money.
Enhancing Collaboration: Test analytics offers collaboration between
testers and developers, and this helps in the improvement of software

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applications, thus providing a seamless user experience.
Accelerating Developer Velocity: Test analytics helps developers and
testers to prioritize test cases with critical functionalities so that
seamless software applications can be developed faster and quicker.
Leveraging Playwright For Test Automation
Playwright is considered a great choice for test automation due to its
following features:
Multi-Browser Support: Playwright allows testers and developers to
test software applications across multiple browser platforms, thus
ensuring it is compatible with all browsers and browser versions.
Speed and Reliability: Playwright offers fast and reliable test analytics
due to its consistent and stable test execution. It also modifies the
network requests, which optimizes test execution and provides a
headless mode to execute test cases without the need for Graphical
User Interface.
Device Emulation: Playwright also has device emulation capability,
which means software applications can be tested across various devices,
thus ensuring they are working as expected on all the viewports. This
also allows the developers to ensure the software applications are
responsive and will provide a seamless user experience.
Test Isolation: Playwright also offers test isolation which means the test
cases are executed independently without interacting with any other
test cases. This ensures that the test cases have not been impacted and
accurate test reports have been created.
Easy Setup: The playwright’s installation and configuration process is
easy, which makes it accessible for everyone, irrespective of their
knowledge and experience.
To leverage the true capability of Playwright test analytics LambdaTest
presents an exceptional solution. It is a cross-browser digital experience
testing platform that integrates with Playwright and offers automation
testing options for web applications and websites.

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With LambdaTest, you can speed up your Playwright testing process and
gain valuable insights into your test runs. It allows business to test their
websites on 3000+ test environments, including real device cloud and
more than 10000+ enterprises.
With LambdaTest, you can run parallel tests across various browsers and
devices, which leads to optimizing test efficiency. LambdaTest’s
comprehensive analytics give you a very detailed report, including test
pass rates, execution times, and failure analysis.
This empowers you to make data-driven decisions for your application’s
quality assurance. You can now level up your Playwright testing game with
LambdaTest’s insightful analytics and enhance your team’s productivity
and overall testing performance.
Key Metrics For Playwright Test Analytics
In order to increase the developer velocity, it is important to identify and
track the following key matrices:
Test Execution Time: Test execution time is the overall time taken to
run a test case. It is important to look at the test execution time to
identify slow tests and thus take necessary actions to reduce test
execution time.
Test Failure Rate: Test failure rates are the ratio of total failed cases to
the total test executed during the software testing process. Knowledge
about test failure rates is important for developing software applications
that are stable and reliable.
Code Coverage: Code coverage refers to the percentage of code that
has been tested, ensuring the critical parts of the software applications
are thoroughly tested.
Test Retries: This metric indicates the number of times the test cases
have been updated and executed in order to pass the testing process.
This helps developers and testers to analyze the stability of the test
environment.

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Average Time To Fix Failed Tests: Including the average time to fix
failed test cases in a Playwright Test Analytics will ensure the efficiency
of the bug resolution process in the software testing process. This will
help developers and testers easily identify the bugs present in the
software application and thus resolve them in less time.
Test Parallelization: In order to improve developer velocity and reduce
the overall execution time, test parallelization is the best metric that
could be used with Playwright Test Analytics. It aims to increase
developer velocity by conducting multiple test cases simultaneously.
Test History Trends: Test history trend is the metric used. In contrast,
test analytics as analyzing test execution trends over time, will help in
maintaining the quality and performance of software applications.
Implementing Test Analytics With Playwright
To integrate test analytics with Playwright, developers need to follow the
following steps:
1. Test Suite Structure: The first step includes grouping test cases and
creating a well-structured test case collection according to their respective
features and functionality. This step helps developers to organize and
analyze test case data more effectively and easily in an organized manner.
2. Test Runner Integration: After structuring the test cases, the next step
involves choosing a test runner, such as Jest, that will easily integrate with
Playwright to generate detailed test reports. The test reports include all
the key metrics, such as failure time and test execution time.
3. Data Collection: The next step is data collection which involves
collecting and storing data from every test execution using an automated
process. The data in this step could be collected using methods like
logging, plugins, or custom reporters, and then it could be stored in a local
or cloud-based database.
4. Data Visualization: Once the data has been collected and stored, it
needs to be visualized using data visualization tools or a custom
dashboard. The data visualization represents the key metrics of the test
cases in the test reports so that they can be easily analyzed and
interpreted.

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5. Continuous Integration: The next step suggests integrating the test
analytics process with the CI/CD pipeline for iterative updation of test data
after every test execution. This step is useful in increasing the developer’s
velocity as the test data is continuously updated without any human
interference and is easily accessible to anyone on the team.
6. Establish Baselines: In order to implement Playwright Test Analytics, it
is suggested to establish a baseline which means setting a value for key
metrics so that the progress can be easily analyzed. It acts as a reference
point for identifying the deviation faced by the software application during
the entire test execution process.
7. Regular Analysis and Feedback: Test analytics is a continuous process,
and therefore, the test report should be frequently reviewed so that
effective and efficient software applications are deployed to the end users.
This will include identifying the issues and defects present in the software
application and thus taking up action to address those issues.
8. Test Optimization: Next step includes analyzing test data so that the
test case suite can be optimized and an effective and efficient software
application can be developed. This step ensures prioritizing of test cases
with a high risk of failure and conducting parallel test case execution so
that overall test execution time can be reduced.
9. Collaboration and Knowledge Sharing: The final step includes
collaboration between the team members involved in the software
development process. This step will ensure every team member has
knowledge about the software application, and their collaboration will help
in developing a quality software application by enhancing the testing
process.
Conclusion
Playwright Test Analytics is a powerful approach used by development
teams to increase developer velocity by just collecting and analyzing data
from the test case execution and tracking the essential metrics.
Playwright Test Analysis helps developers to know about the total
execution time of a test case along with other details such as what are its

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code coverage and failure rates so that developers can take necessary
actions to develop a quality software application in less time.
Also, it helps to identify issues and defects present in the software
applications so that they can be fixed at the early stages of the software
testing process. Thus, the end users receive a seamless software
application.
In the end, we can conclude that in this advancing software technology
era, it is beneficial for developers to use the Playwright Test Analytics
approach so that they can develop and deploy an effective and efficient
software application.
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