January 29, 2021

Leena Sanap


Artificial Intelligence (AI) is hands down, one of the most revolutionizing technologies developed by humans in the recent past. It has opened the doors to incredible progress in every area of human activity imaginable. An AI-enabled machine can think and reason for itself, quite similar to that of a human being. It can work with massive amounts of data, use it to extract insights, and modify its future behaviour accordingly. Through this machine learning AI has helped many businesses to stride ahead in the global market.

Among many things, Artificial Intelligence and machine learning have also had their impact felt in the area of Software Testing. It has expanded its scope and is in the process of making it more intelligent and efficient. The application of reasoning and problem solving helps in automating and enhancing Software Testing.

A couple of decades ago, Software Testing was merely an act of compliance that received little attention. But today, the cloud, microservices, DevOps, and IoT have completely revolutionized the field of software development. New software are being released at a tremendous pace. In today’s fast-paced technological environment, to forge forward, manual testing simply cannot be an option. It is where the application of AI in Software Testing becomes indispensable.

Similar to the effects it had on businesses, Artificial Intelligence has minimized time-consuming and mundane tasks to a great extent in the field of Software Testing too. Hence, It has made it possible for software teams to focus more on tasks that matter to them.

What does Artificial Intelligence in Software Testing have to offer?

The application of AI and machine learning in Software Testing is growing. According to the World Quality 2019-20 report, it will soon transform the testing environment in a positive manner. Let us see how this new approach to testing will change it and make it better.

  1. Intelligent automated testing - The application of Artificial Intelligence accelerates time and delivery speed. Thus, complex technical tasks with shorter deadlines can be completed in very less time. Take, for example, the Facial Recognition Technology that needs to be repeatedly monitored and checked in the mobile development stage. The pixel structures of the UI are identified with text or image patterns. It needs technology that can identify the images and text on the screen. Artificial Intelligence can accomplish this way faster than anyone else. Applications can be accessed by the AI just like a user. Hence, from the perspective of the user, the best way for an efficient app or Software Testing is through an automated engine.

  2. Smart testing coverage and detection of bugs - There are countless paths through a complex app. We need to understand and identify which of them can be used for automation. Their selection and identification can be done using Artificial Intelligence classification algorithm networks. During the process, AI bug detection algorithms keep learning from data correlations to enhance the Software Testing coverage. Hence, it makes it easy for developers to detect and troubleshoot causes and effects in the software.
    AI bug detection algorithms hunt defects. They are according to user journeys, which are automatically produced. Artificial Intelligence thus greatly helps in expanding coverage and efficiency through its bug detecting and coverage algorithms.

  3. Enables easy and efficient software development lifecycle - Machine Learning Artificial Intelligence is widely used in popular applications like SIRI and Google Assistant. One of the major impacts of AI application in Software Testing tools is enhancing the software development life cycle. AI-enabled automation in Software Testing through its problem-solving and Machine Learning speeds up time-consuming tasks and eliminates the challenges and limitations of Software Testing tools. The software developers and testers need not be continuously engaged in tasks of redundant nature. The automatic running of tests on source code changes is checked. The developers and testers are immediately alerted if the tests fail.

  4. Continuously testing, learning, and predicting - Software Testing is a continuous process. It needs to be continually monitored and improved over time. An Artificial Intelligence algorithm continuously monitors and watches the test results throughout the period. In the process, it keeps on learning and looks at trends. AI learning algorithms enable predictive analysis through the building of decision trees. They can effectively identify defects in a particular workflow that may ultimately result in the closing down of the entire system. Thus it helps in troubleshooting issues promptly before they go out of hand.

  5. Effective and complete Software Testing for executing tests - Application of Artificial Intelligence in Software Testing tools helps them learn how to recognize the different controls during scanning and executing in the process of execution. AI helps in testing color, control size, and text alignment. AI tests the process of generalization of images to the software, adding new images, and adapting current image patterns by the software. Artificial Intelligence is made use of in checking and testing controls like that of the scrollbar during the development phase of the mobile app. It reduces the time spent in identifying and recognizing them during the stage of their technical implementation. AI can recognize more effectively in comparison to manual testers, the controls that are changed. Thus, Artificial Intelligence implementation to Software Testing optimizes the software value to a great extent.


Artificial Intelligence is here to increase the productivity of the Software Testing process. AI in Software Testing greatly optimizes the functioning of Software Testing tool suites. It helps testers accomplish their objective of testing their code more efficiently and ultimately creating cutting-edge software quickly. It reduces all the manual tasks that the testers have to otherwise engage in. It enables them to design new, sophisticated tests for software with advanced functionalities.

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