AI in Software Testing | Why it is Important In Software Test Automation?
In layman terms, AI or Artificial intelligence is the area of computer science that focuses on making machines “intelligent.” And by intelligence, it means making the system capable of learning and making decisions like humans. Thus, an AI-enabled machine will be able to learn how to react in particular situations and then make decisions in the future according to its learning.
Though AI implementation is still in its early stages in most fields, it will take a long time to work perfectly. AI technology today can help with many everyday tasks that don’t require complicated thinking.
Let’s discuss artificial intelligence in testing.
Table Of Contents
- 1 AI in Software Testing
- 2 How AI Changing the Dynamics of Software Testing?
- 3 AI in Software Test Automation
- 4 What Tasks can AI help with?
- 5 What Tasks can AI not help with?
- 6 What does AI mean for QA Testers?
- 7 Is AI Vital for Software Test Automation?
- 8 AI Tools in the Market
- 9 Conclusion
- 10 Frequently Asked Questions
- 11 Suggested Reading
AI in Software Testing
Like most industries, the software testing industry too has evolved a lot since it started. Earlier, the development cycles used to be longer in terms of duration, so the testing time was longer too. Still, with the rapidly evolving market, the need of the hour is to reduce the development, testing and deployment time and effectively release new versions as quickly as possible. Refer Test Automation To Achieve Fail Fast, Fail Often. The organization must use test automation for everyday development, testing, and deployment tasks. They need to recognize which tasks are the same or similar and see if they can be automated. For example, when it comes to software testing, QA testers have to do many of the same things repeatedly. Automating these tasks will be helpful in the end. An example is running a set of tests each time something gets deployed.
In addition to just the mundane and repetitive tasks, there are also those tasks that are similar with minute differences which, if automated, will make the life of a software tester easier. An example of such a task is the maintenance of automated UI test cases that fail every time there is change. Say, when the name of a UI element is changed, the element’s name needs to be changed in the test automation tool, and the test case will run fine.
Tasks in the life of a QA tester(Manual and Automation)
Tasks in the life of a QA tester can be broadly summarized as below:
- Initial Review of Documentation – Business Requirements and any development related design or implementation document.
- Test Plan and Test Case creation – These test cases are created after reviewing related documentation, business requirements and development details.
- Execution of Test Cases
- Test Result Reporting
- Testing before releases
- Testing specific to changes
- Performance and Load Testing
- UI and UX testing
- Automation of test cases
- Maintenance of automated test cases,
How AI Changing the Dynamics of Software Testing?
AI is revolutionizing software testing, making it faster, more accurate and efficient. AI-powered tools can analyze tons of data and automatically create test cases, freeing up time for other tasks. Plus, AI can predict potential issues before they become problems so teams can proactively address them. Ultimately, AI is transforming software testing from a labor-intensive and error-prone process to a quicker and more reliable one.
AI in Software Test Automation
AI is revolutionizing software test automation, making it easier, faster, and more accurate. AI-powered tools can create test cases, run tests, and analyze results automatically – saving time and effort. Plus, AI uses machine learning algorithms to learn from past tests to improve test case quality. AI in software testing helps teams cover more ground with fewer mistakes at a lower cost.
What Tasks can AI help with?
Most of the above tasks have a factor of repetition and redundancy in them. That is why the QA industry has a lot of scope of automation. Also, there are tasks that, if learnt once, can be quickly done by AI. Some such tasks are:
- Creation of test case for one field: The AI software needs to recognize what test cases need to be run for one field type and automate them for execution.
- Execution of test case according to changes: Once the AI software knows what areas have changed in the code, it can do risk analysis and decide what test cases should be executed to ensure nothing is broken automatically before the release.
- Test Planning: Planning what test cases should be created for a new feature and how the test cases should be executed.
- Automation of the same type of workflows: When the tester automates one workflow, the AI software can learn to automate all similar flows saving a lot of crucial time.
- Maintenance of test cases when there are changes in the code: The test cases that are broken by a minute change in the code, like just renaming a component, can be easily rectified by an AI software.
- UI testing: According to the UI components, AI-based software can easily generate test cases that test all the UI workflows.
- Performance and Load testing: Generation of load for performance and load testing
- Testing before releases: AI-based software can decide what all test cases should be executed before what kind of releases according to the changes in the code and new features built.
- Create test plans automatically.
More on the scope and areas AI can be applied are discussed in the blog Scope and impact of AI in Automation Testing.
What Tasks can AI not help with?
AI can help with repetitive tasks more or less similar to those done earlier. But even if AI is employed to its full potential, some tasks will require human efforts; below are few such tasks:
- Review of Documentation: Examining the documentation to understand the in and out of a system that needs to be built is better done by a human.
- Test Creation for complex scenarios: The complex scenarios that involve using multiple components in different ways is better done by a QA tester because, ultimately, the application will be used by humans.
- Test Result Reporting and analysis: Understanding the test results and deciding the next steps needed.
- UX testing: User experience can improve when users go through the application.
What does AI mean for QA Testers?
Just like automation reduces the manual repetitive work for QA testers, AI also aims at minimizing the repetitive work but with added intelligence. It aims to learn solutions to problems in the future by learning the solutions to similar problems faced in the past. This means that the QA testers should keep doing what they are already doing but also have knowledge of AI-based test automation tools and use them to improve their and the QA team’s efficiency.
Thus, the overall quality quotient of a product to be launched can be significantly increased in much less time if QA Tester can use AI tools effectively.
Is AI Vital for Software Test Automation?
It is estimated that about 70% of the tasks a Manual QA tester performs can be automated via AI. When 70% of the tasks are automated without a huge initial learning curve. It translates to profit sooner and results in an increased ROI, which means that AI will prove important for Software Testing especially in Software Test Automation.
AI Tools in the Market
A few popular AI-powered test automation tools go beyond the industry-standard Selenium, like Testsigma & more.
Testsigma is a cloud-based continuous testing tool that uses NLP for test case creation and an AI-powered core for maintenance of all automated test cases. It has made the automation of test cases so easy that all stakeholders for a project, like project managers, product managers, developers, can be involved in test case automation.
AI is changing how software testing works and making it easier to automate. It uses machine learning and predictive analytics to help testers find bugs faster and more accurately, saving time and money. AI-powered tools can generate test cases, analyze results, and spot potential problems before they cause real damage. AI is a must-have for any software development team looking to stay ahead of the game in this digital world. Testsigma is the perfect platform for teams who want to use AI in software testing. It has an easy-to-use interface, even if you’re not tech savvy, and can help you automate the whole testing process. From creating test cases to analyzing results, Testsigma takes care of it so you can focus on other important stuff. Plus, it works with different testing frameworks and integrates with many other tools – so whatever your team needs, Testsigma has got you covered! If you’re looking for a way to speed up your software testing using AI, Testsigma is the place for you!
Frequently Asked Questions
Will AI take over Software Testing?
No, AI will not replace human involvement in software testing. While AI can assist in automating certain aspects of the process, humans will still be necessary for tasks such as formulating test cases, providing context, and making judgments on test results.
What are the Benefits of AI in Software Testing?
The benefits of using AI in software testing are awesome! It can make testing faster, more accurate and cheaper. Plus, AI can even spot issues before they become a problem and help you figure out what tests to run first for the best results.
How is AI used in Automation Testing?
AI is used to make testing more accessible and more efficient. It helps automate the testing process, such as creating test cases, running tests, analyzing results, and spotting errors. AI-powered testing tools use machine learning algorithms and predictive analytics to create tests automatically, find bugs or security issues, and even get better at finding problems over time by learning from past test runs.
Top 20 Online Automation Courses
Build Test Automation Framework
Agile Automation Testing in SaFe