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Intelligent Test Automation

As I was browsing through the internet, I came across an article about Intelligent Test Automation (ITA), I was intrigued to learn more about it. In this blog I would like to share with you what I understood from that article.

In today’s world, most companies are integrating AI and ML for their testing efforts to produce test automation solutions. In this competitive world, the companies feel that this will help them stay ahead in the game.

Before we understand what ITA means, lets dive in and understand what AI and ML means.

AI stands for Artificial Intelligence, the science of creating machines capable of mimicking human thought processes, enabling them to perform tasks deemed intelligent. Unlike humans, AI technology has the ability to analyze vast quantities of data in different manners.

ML stands for Machine learning and is a subset of artificial intelligence (AI) that involves developing algorithms and statistical models that enable computers to perform tasks without being explicitly programmed. Instead of relying on explicit instructions, machine learning systems learn and improve from experience, using data to identify patterns, make predictions, or generate insights.

Integrating both AI and MI into the testing process helps ITA to improve the efficiency, accuracy and flexibility of tests. This goes beyond the traditional automated test methods. In ITA, testing follows a model-based approach where the application is described in business or functional terms. The AI model in your test automation identifies these parts and conducts tests automatically. You don't have to list out the tests; instead, the AI model generates and performs the tests when it recognizes the components.

While there are real-life instances of AI impacting testing procedures in organizations, there are constraints on the extent of AI's capabilities.

Let’s examine the present situation to understand the actual potential and limitations of AI in this context:

  • Scriptless or No-code Automation

Automation of your test need complex test scripts but AI is changing that. No code or Scriptless test automation for web applications and mobile is becoming popular among testers as AI helps in building and running test cases in plain English which requires no technical expertise and allows business users to work with automation. This scriptless approach saves time, produces accurate and smart results thus reducing the software development life cycle span.

  • Self-healing Test

As name suggest, these tests can self-heal without any manual help. But they rely on intelligent tools to maintain themselves. Automated tests alone don't identify and resolve failures autonomously; however, platforms like Testsigma empower self-healing capabilities for such tests. Testsigma is equipped with AI capabilities to detect abnormalities in the source code and fix it so the tests don’t fail.

  • Voice Assisted Test

The voice assisted tests are relatively new and behave like other voice assistant but this only caters to testers.  Test steps are created based on the voice instructions. For example, in case you want to validate a user registration page then you need to start giving commands or instructions to the voice-based tools and they do the rest. It may sound simple but this scenario is quiet challenging since not all tests are simple especially validating a user registration page. Also, for giving voice commands for complex test scenarios may prove to be really inconvenient and difficult. Another situation is when a test has multiple steps, then we need to voice them separately. We also need to be careful while pronouncing and communicating since even a slight mispronunciation or miscommunication will break the entire test case.

Now, let’s look at why Intelligent Test Automation is being adopted by various teams. The usage of ITA outweighs the benefits when you compare with the traditional approach of automated testing. Some of the benefits are outlined below:

  • Speed and Efficiency:

ITA makes testing faster and more powerful since it uses both AI and ML. It speeds up the creation, execution, and analysis of test cases, cutting down on testing cycles and time-to-market.  Automated test scripts adjust to code modifications, enabling continuous testing and faster feedback loops.

  • Better and Smarter Decisions:

Data is crucial. Even in testing, success hinges on the use of test data to drive the process. When combined with AI, testers are able to make superior and intelligent decisions using these smart data.

  • Competitive Advantages:

In a competitive market, swiftly delivering software without sacrificing quality can be a game-changer. Organizations embracing intelligent test automation gain a notable edge by efficiently delivering high-quality products at a faster pace.

  • Predictive Testing:

Picture a scenario where you foresee possible defects before they escalate into costly problems. Intelligent automation utilizes past data and AI algorithms to anticipate areas of weakness, facilitating proactive testing and risk reduction. This underscores the significance of utilizing intelligent data to obtain actionable insights.

  • Complex to Easy

AI test automation is making testing easier for everyone.

We have seen why ITA has been adopted and the various advantages but with every advantage comes challenges too. The various challenges are:

  • For startup, initiating the process might be intricate and demand significant resources. Setting up the required infrastructure and integrating it with current systems can pose challenges.

  • The technical skills required to develop intelligent automation scripts must be advanced, leading testers to acquire proficiency in AI and ML language models.

  • The above two steps mean it may prove to be demanding in terms of time and resources.

  • Training certain ML models with extensive and varied datasets might be necessary. Acquiring and organizing these datasets can present a significant challenge.

  • The complexity of managing updates and integration of intelligent automation tools with existing testing frameworks, CI/CD pipelines or external tools needs to be considered.

  • AI-driven test automation might generate false positives (identifying non-existent issues) or false negatives (overlooking real issues).

To conclude, Artificial intelligence holds boundless potential, and with each passing year, the application of AI algorithms in intelligent test automation becomes more advanced. While many AI test automation tools are still in their early stages, the significant potential for AI to transform the future of software testing is evident.

Hope this blog gave you some insight on Intelligent Test Automation and how it will play a major role in the coming days.

Thank you for taking the time to read through this.

Bye until I meet you all with my new blog soon!


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