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What is Automated Regression Testing?

Automated regression testing is a software testing process. It repeats previously developed test cases to ensure that recent changes in test cases or enhancements to the software have not negatively affected its existing functionality and efficiency.

In other words, there is always a risk of damage to existing features when new code is added or changes which are made to a software application. Regression testing helps to tackle this risk by systematically retesting the software parts.

Automation in regression testing means using automated test tools or scripts to run test cases instead of manually. This process increases productivity and accuracy. As it allows repeated testing without the need for manual interference. Automated regression testing is particularly useful in projects that require frequent updates or ongoing integration/deployment processes. In this manual testing alone may not be the best option due to time consumption and the number of tests required in regression testing.

Why choose automatic regression testing?

Regression testing is one of the important ways to ensure that recent changes to your software have not negatively impacted existing services and software performance but manual regression testing can be time-consuming, error-prone, and costly. While automation testing can significantly reduce testing time and testing costs. Moreover, it will improve the efficiency and accuracy of the test.

Best practices for automating regression tests

Comprehensive Test Coverage: During the software testing process, you should prioritize the basic functionality and features of regression testing. It will greatly improve your independent testing and help you focus on the areas of greatest impact.

Modular Test Design: You should divide your test case into smaller reusable modules. This modular approach can increase the maintainability and scalability of the trial and facilitate efficient trial design and execution.

Version Control Integration: Regression testing should be integrated into version control systems such as Webomates to facilitate integration with test management. This will ensure that your test is always up to date with the latest regulatory changes.

Continuous Integration (CI) Pipeline: Add regression testing to your CI pipeline to identify problems early in the development lifecycle of software. It integrates easily with popular CI/CD tools and enables automatic testing as part of your workflow.

Regularly maintain testing: Update and maintain your automated test case regularly to keep up with changes to the application that is tested. A good interface simplifies testing and maintenance tasks. It will ensure that your testing remains robust and reliable over time.

Automated Visual Regression Testing

Visual regression testing is essential to detect visual changes during software updates. It offers advanced testing capabilities. Also, it will allow you to compare visuals of your application across different platforms and applications. With pixel-perfect accuracy and a stable comparison system, you can detect optical anomalies with accuracy and reliability.

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Welcome to the new world of ChatGPT.

The world is on the cusp of a major revolution, with the rise of generative AI technology. And at the heart of this revolution is ChatGPT, an advanced language model that is capable of creating code, detecting code vulnerabilities, crafting poetic verses, and even clearing competitive exams. In just a few short days since its highly anticipated release, its versatile nature has already left users impressed.

How can ChatGPT help in Automation Testing?

We are currently riding a wave of business transformation where we are merging ChatGPT with every business. With its remarkable ability to understand natural language and generate context-specific responses, ChatGPT can provide valuable insights for QA teams and is poised to become a game-changer in the world of software testing.

It can assist you in creating a test strategy by providing a wealth of information and insights to help ensure that the testing process is well-planned, effective, and aligned with the project goals and objectives.

1. Generate test cases

The process of creating test cases is a tedious and time-consuming task. It requires a keen eye for detail and an in-depth understanding of the application or feature being tested. With ChatGPT’s assistance, this process can be made simpler and more efficient.

When it comes to testing software products, it’s not just about creating a list of test cases. It’s also about ensuring that the test cases cover a broad range of positive and negative scenarios. You can also take help of ChatGPT to create a set of positive and negative set of test cases.

2. Generate UI Automation Script in different languages

Although ChatGPT does not generate UI automation scripts directly, it can provide invaluable support to automation engineers by generating detailed and well-structured test cases.

3. Test result analysis and reporting

One of the most important steps in assuring the quality of a software product is analysing test results. ChatGPT can assist in the analysis and sharing of test results, offering insightful information on possible areas for improvement or failure analysis.

You can prompt the tool to include metrics like test coverage, test execution times, and the number of defects found in the report. Furthermore, as test results accumulate over time, ChatGPT can also assist in noticing patterns and trends. This can help you to use this information to pinpoint areas that may need additional time or effort, enabling you to focus efforts on the most important areas first. Read more about:https://www.webomates.com/blog/how-chatgpt-is-revolutionizing-software-testing/

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Software Testing Life Cycle

Test Automation vs Manual Testing

Selenium Testing Automation

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Software testing is an imperative step in the Software Development Life Cycle (SDLC) that guarantees customer satisfaction — the only premeditated way in test automation where an application can be observed under certain conditions. The testers then try to identify the threshold levels plus the risks involved in the code implementation. Software debugging can be carried out once an application has been thoroughly tested.

Testing, however, is slowly moving towards greater automation so that maximum accuracy can be achieved. In order to achieve greater automation companies are turning to Artificial Intelligence( AI) AI is the science of getting computers to perform tasks that require human intellect in a traditional scenario. This implies that instead of manual testing, we are slowly, assigning machines, the tasks of writing and performing the tests.

AI gives a machine the ability to process information about its surroundings, work ‘intelligently,’ and learn to adjust to the changes. As this field begins to gain ground, programmers worldwide are trying to unearth its potential in software testing.

AI bots can effectively interact using human inputs and be trained for more complex tasks like testing software. AI will prove to be beneficial in increasing the efficiency of the testing processes.

Why do we Need AI in Software Testing?

Many a time, developers are unable to carry out thorough testing of an application due to the lack of time and resources. In such a case, there comes a need for a system that could rationally identify areas that need more elaborate attention available through automation based on repetitive patterns.

Software testing ordinarily consumes a tremendous amount of time, human resource, and funds. Furthermore, with developers seeking faster deployments, AI is the right step forward. Since a majority of testing is a mere repetition of some checks on the software, AI is better armed to automate it with efficiency rather than a human tester who needlessly raising the costs and effort.

A better practice would be letting human intellect and energy be used in identifying the application problems by designing unique and innovative test settings. Therefore, it is ideal to leave only a smaller percentage of the testing operations to humans and relish the magic of AI. This would also, ensure higher quality and reliability outcomes. Read for more

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