Is AI Really Important In Software Testing?

Artificial Intelligence (AI) is one of the most popular buzzwords in the software development industry. AI helps a machine or a program to learn. Experts believe that the most important solution to overcome the increasing testing and QA challenges will be the emerging introduction of machine-based intelligence.Therefore, looking into the scope of Artificial Intelligence and Machine Learning I would suggest every beginner go through this Machine Learning Online Course. Kindly let me know when link goes live. After this deal I will get you few more clients

Not just in Test Development, but AI is also used to recover from failures, auto-heal and maintain the changing UI elements on an application page, find what other tests are affected due to the change in UI. AI makes a tester’s job easy with less human involvement and more supervising.

AI in Software Test Automation in 2019

According to Gartner, AI-drive development is one of the top three strategic technology trends of 2019 having the potential to drive testing.

Software testing is an ideal field for applying AI. Testing incorporates lots of human and machine-generated data. With AI, developers can facilitate decision making, drive automation and improve efficiency in the area of testing. The main application of AI in software testing is to focus on making software development lifecycle easier.   AI can be used to automate the mundane and tedious tasks with the application of machine learning, reasoning and problem-solving.

Though human intelligence is still very much required in applying business logic, the value of AI comes from decreasing the direct involvement of the developer or a tester in a majority of the mundane tasks.

How AI will affect Testers?

With AI, software testers will transform from being a QA team to a test automation team because AI is going to make software testing more efficient. With almost 70 percent of testing being repetitive, AI can quickly occupy the space and a lot of things are going to change in the testing field.

Human testers don’t have a reason to worry about their careers. They only need to start thinking differently about the future of the testing.

AI is the next big thing, but it won’t replace humans. AI will execute the number of tests faster but humans will still supervise the outcome of the test, as AI can only execute explicit testing whereas implicit and latent testing has to be performed by testers. Human testers will see the more interesting parts of software testing by working in harmony with AI. The software testers working hand-in-hand with AI can revolutionize the way we test today.

Need For Tools to Evolve

With the adoption of Agile and DevOps methodologies, software development and software testing has evolved a lot and will continue to do so in the era of AI, as well.

With AI, the testing activities which were performed manually before are now being handled by the machine which helps get more variations of the test and execute more tests that a human could supervise. AI and Machine Learning mainly depend on training software to understand input data versus output.

Even without AI, the testing tools have evolved. In this era, tools help testers in n number of ways including creating, organizing, prioritizing test cases, managing tests and their outcomes efficiently, and giving developers the right feedback they need.

AI will help developers and testers to do more with less making work more fun. Smart tools can eliminate the manual and repetitive nature of the testing job making their job less complex and effective.

Advantages of AI in Software Testing

  • AI helps organizations to test smarter at an accelerated pace.
  • Organizations can leverage AI to drive automation by using both supervised and unsupervised methods.
  • AI can be employed for object application categorizations for all UIs. AI-enabled technical maps can be created that can look at the GUI to easily obtain labels for different controls.
  • AI-powered testing platforms can easily recognize the constant changes or updates in the algorithms promptly.
  • AI can help identify which tests to run first when resolving key issues. It saves a lot of time which goes into making a fix, thereby reducing the loss of revenue during data loss caused due to security loopholes.
  • AI can be used to conduct exploratory testing within the testing suite. Risk preferences can easily be assigned, monitored and categorized with AI.
  • AI can help testers in creating the right heat maps to identify key issues in processes and increase test accuracy.
  • AI can also increase the overall test coverage, apart from the depth and the scope of the tests. Since 80 percent of all the tests are repetitive, AI can help reduce the software tester’s time and help them focus on the creative side of the testing. AI can be used to enhance UI testing, defect analysis, traceability, rapid impact analysis and determine pass/fail outcomes for complex and subjective tests.
  • AI can be leveraged to identify patterns in application testing, non-functional analytics, social media data analytics, and efficiency analysis.
  • Machine Learning can be used to test programs and to get robust test data and insights which help make the testing process more in-depth and accurate.
  • NLP based Testing is the latest advancement in the Testing domain. NLP based testing scripts simplify test automation and are less flaky compared to recorded scripts. 

There are fundamental ideas about testing and quality that only humans can understand and are here to stay. With AI finding its way into software and automation testing, humans will focus more on creativity, analysis, exploration testing which is also important & understanding the application. AI could become a valuable tool to ensure IT organizations deliver quality with each software release.

AI-driven Testing Tools leverage AI to reduce maintenance effort and time by many folds which results in improved productivity. These tools help you easily identify affected tests, repetitive and potential failures upfront. AI in software testing can indeed save a considerable amount of time, money and resources and help testers focus on the most important objective, i.e. to release great software.