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Being one of the most critical phases of the software development life cycle (SDLC), software testing requires speed and accuracy to ensure fast delivery of the final product. Software testers need to quickly submit their feedback to the development groups and roll out bug-free updates in the minimum possible time to stay ahead of competitors.

Given the required speed and complexity of software development projects, technology companies continuously search for resources to test intelligently and quickly. Those enterprise leaders who understand the importance of speed in software development strive to shrink the product delivery time without compromising on quality. In this context, AI-powered automated testing can be used to characterize the recognized controls while framing tools to reduce testing time and improve the software’s reliability.

Given below are a few examples of integrating AI in software testing.

API Testing

API testing is essential for a better user experience. With artificial intelligence, software testers can understand the patterns and relationships in different API calls, identify test parameters, and create a series of API calls. Based on the analysis of user behavior, advanced patterns and inputs can be produced for API testing with artificial intelligence. The generated tests improve the quality of the application user interface (API) and cover more edge cases.

Unit Test Generation

AI plays a key role in unit test generation during software testing. By performing the static analysis of the software, testers can use AI to identify the areas of code that remain uncovered and subsequently create unit tests. Besides, unit tests created by AI-assisted tools don’t need any setup or interactions and could be updated in the future every time the source code changes. Consequently, AI can help developers quickly maintain unit tests without spending so much time.

Continuous Testing

The AI-powered continuous testing process allows software testers to automatically detect the altered controls and observe even a single minute change. Users can identify the tests to get the greatest coverage with limited time by using risk-based automation. Likewise, the testers can integrate AI with test creation, execution, and data analysis to identify the controls, manually update the test cases, and effectively identify the connection between the defects and components.

UI Testing

AI-powered automated testing helps in the object application classification for UI. The categorization of recognized controls is performed during the creation of tools. This allows the software testers to pre-train controls and create a technical map such that the artificial intelligence is looking at the GUI to acquire labels.

Examples of AI-driven Testing Approaches

Four primary examples of AI-driven software testing approaches include:

1. Visual Testing

Allows users to compare screens and leverage image-based learning to test the application from an end-user’s perspective.

2. Self-healing Automation

Used to auto-correct element selection in tests whenever there is a change in the user interface (UI).

3. Differential Testing

Used to auto-correct element selection in tests whenever there is a change in the user interface (UI).

4. Declarative Testing

Helps the software testers to determine how to carry out the test and specify its intent in a natural or domain-specific language.

AI-based Test Automation Tools

 

Sr. No.

Test

Function

Type

1

Selenium

Automate web applications across different browsers

Open Source

2

Appium

Native, hybrid, cross-platform web application testing

Open Source

3

Citrus Framework

Automate integration tests for any messaging protocol or data format

Open Source

4

Katalon Studio

Web, Mobile application, and API Testing.

Open Source

5

JMeter

Perform load and performance testing for web services, mobile and, API.

Open Source

6

Robot Framework

User acceptance testing and acceptance test driven development

Open Source

7

Serenity

Write automated acceptance and regression tests

Open Source

8

Appvance

Generate test cases based on user behavior

Premium

9

Functionize

Faster test execution

Premium

10

AutonomIQ

Speed up test cases, data, and script formation.

Premium

11

Parasoft SOAtest

Alter manual UI tests into automated API tests.

Premium

12

Testimo io

Authorize, execute, and maintain automated tests

Premium

13

Test.ai

Analyze the performance metrics on application

Premium

14

Applitools Eye

Ensure the web and mobile apps are working as expected

Premium

15

Mabl

Find regression tests, simply automated test creation, and automatically maintain the tests.

Premium

Benefits of AI-powered Automated Testing

Here’s how integrating artificial intelligence in software testing can benefit companies.

Improved Accuracy

Irrespective of their skills and expertise, humans are always vulnerable to error. Even highly qualified and experienced software testers make mistakes in manual testing. Sometimes, these mistakes could result in huge financial losses or badly impact the company’s brand reputation. To reduce the likelihood of errors in manual testing, artificial intelligence (AI) should be used during software development.

AI-powered automated testing allows the testers to perform their process with accuracy and reliability. Software testers can free themselves from tedious manual testing steps and cope with complex features. This way, they can save additional time for innovation and record thorough outcomes.

Increased Speed

In manual testing, software testers cannot implement a controlled web application test for multiple clients simultaneously. They have to run each test separately that increases the testing time as well as the chances of making a mistake. On a commercial scale, delayed product delivery time means disaster for a company’s reputation and competitiveness.

With AI-powered software testing, QA engineers and testing professionals can reenact a large number of virtual sets of clients collaborating simultaneously with web-based applications. The technology helps software testers complete the same recurring tasks effectively again and again as per the project planning. By making artificial intelligence work on tedious tasks, the professionals can focus on exploratory tasks like R&D and the creation of effective automation solutions.

Increased Test Coverage

Innovation and diversity in the final product are key to success for tech companies in today’s highly competitive business environment. To improve the software quality, testers often search for resources to enhance the overall scope and depth of tests.

With AI-powered automated testing, IT professionals get insights about program states, data tables, and file contents to investigate the behavior of the software product. They can perform thousands of distinctive tests with test automation and increase their test coverage that cannot be achieved with a manual testing process.

Team Collaboration

Artificial intelligence improves teamwork among the development team and encourages professionals to collaborate to find solutions to complex problems. Software developers, DevOps engineers, designers, and testers can use shared automated tests to timely identify the troubles and take steps to remove those errors. They can run tests automatically each time the source code changes and check whether it succeeded or not.

Timely identification of bugs through teamwork can increase the developers’ confidence and significantly improve product quality.

Cost-efficiency

In a manual process, companies have to repeat software testing each time source code is customized that is time-consuming and relatively more expensive. On the other hand, testers can implement AI-powered automated tests again and again without spending more time or resources.

Consequently, they can significantly reduce and time of software testing and cut overall project costs without losing the software quality. By using AI testing tools, tech enterprises can release products on a daily or weekly basis and gain an economic edge over their competitors.

The Takeaway

Artificial intelligence (AI) has been a buzzword in the global tech industry for years. The technology is being used in the software testing process to improve the product quality and increase time to market. Similarly, companies use AI-powered automated testing in item application categorization for user interfaces and characterize recognized controls.

Additionally, artificially automated testing enables software testers to pre-train controls and make a technical map to get labels for the particular controls. This way, using AI in tedious software testing can improve software’s accuracy, increase testing speed and coverage, encourage teamwork, and reduce overall project costs.

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