Artificial Intelligence is a diverse field of computer science concerned with developing smart machines able to perform tasks that mainly require human intelligence.
AI is defined as an interdisciplinary science with various approaches, but improvements in deep learning and machine learning are creating a dramatic transformation in each sector of the tech industry virtually.
AI makes it easier for machines to perform human-like tasks. It allows machines to make adjustments to new inputs and helps them learn from experience.
Most AI examples that you may know today are self-driving cars, chess-playing computers, Amazon Alexa that are heavily dependent on deep learning and natural language processing. With the use of such technologies, it becomes possible to train computers for completing particular tasks by recognizing data patterns and processing large amounts of data.
Few Similarities Between AI and Test Automation
Both AI and Test Automation suffer from controversy and misconception infused by bad media coverage and poor marketing.
Both are trying to reduce the work pressure of humans. Test engineers have doubts in their minds that test automation can replace them from jobs.
However, some non-testers grab the information from the media that AI can take over a significant percentage of human vacancies. It is not easy to say what will happen in the future because the report of Mordor Intelligence is explaining something different to us.
According to them, the automation testing market is projected to grow at 14.2% during the forecast period from 2021 to 2026.
Due to the demand for automation for the testing processes and solutions in order to give seamless experiences to customers, the growth of the automation testing market will increase with cutting-edge AI technologies for software testing environments.
In the coming years, we can see more use of artificial intelligence in software testing due to its capability of reducing the test life cycle. It can be used in several aspects of testing, including functional testing, regression testing, automation testing, and performance testing.
Factors like increasing complexity for the transition execution from manual to automated testing are likely to prevent the market development growth. But with the blend of Machine Learning and Artificial Intelligence, the steering of automation testing can better manage.
Check-out How AI in Test Automation Seems Similar As Per the Work Roles:
AI automates repeated learning and exploratory through data:
No doubt, AI differs from robotic automation and hardware-driven automation. AI doesn't feel tired like humans.
It can perform computerized and high-volume tasks reliably rather than automating manual tasks. AI can automate repetitive learning processes and exploration through data; still, it requires humans to ask the right questions after setting up the system.
Similarly, in automated testing, software tests have to be always repeated during development cycles to satisfy quality needs.
Whenever any modification is made in the source code by the software development company, their experts prefer to repeat the software tests before each release. They test the compatibility of the software with all hardware configurations and operating systems so that one can use it in a better way.
Manual repeated tests are time-consuming and costly. Whenever it comes to run any test again and again without spending additional cost, automated testing becomes the right choice as it can minimize the time to run repetitive tests from months to days and days to hours. Also, it helps save money and effort.
AI helps in achieving accuracy:
Acquiring accuracy with AI is possible through deep neural networks. For instance, Google search and your interactions with Alexa are based on deep learning.
Deep learning can deliver super accuracy to humans for image restoration, image classification, image segmentation, and object detection.
It can even recognize the handwritten digits. In deep learning, manual neural networks are used to teach machines and automate the activities performed by human visual systems.
Many people consider AI-driven test automation, techniques, and deep learning in the medical field to prepare the MRI reports of cancer patients because it gives the same accuracy as highly experienced radiologists.
Even after having too many certifications in software testing and skillset, most proficient testing teams make mistakes while testing any software or application manually. With automated tests, it is easy to execute the same steps many times with the recording of detailed results.
Therefore, by incorporating Artificial Intelligence in Software Testing, testers can keep them free from executing repetitive manual tests. Instead of this, they can better focus on the creation of new automated software tests and have the ability to tackle complex features of the application.
Deep data analysis:
The neural networks that have many hidden layers help AI to analyze more and more data in detail.
Previously, no one had ever imagined that deep learning and AI could help build fraud detection systems. But Danske Bank and TeraData launched an AI-based engine to detect frauds in real-time. The power of Big Data and incredible computer systems have changed many more things in the 21st century.
Whether we believe it or not but AI comes to improve the quality of products and performs the tasks with accuracy.
There may be a need to collect lots of data to train the deep learning models because they work after learning directly from the data. The more data one feeds to the deep learning models, the more accuracy you can achieve for the software.
Moreover, one can enhance test automation with AI-powered tools like when using the Appitools that is an application designed for visual management, UI testing, and software monitoring, it ensures that the UI of the app will be visible accurately to the users. It allows you to place the UI elements in the right size, colour, and position.
Applitools is an end-to-end software testing solution powered by visual AI to help test automation engineers, manual testers, Devs, and QA managers to reduce cost, accelerate delivery, and increase the quality of software products.
AI and ML algorithms can read your application and even have an understanding of it. These algorithms develop data sets to have an observation of your application. It even helps you understand how the particular feature behaves in a specific condition.
By using AI/ML algorithms, it is easy to create test cases and record the expected results automatically. The learning power of AI algorithms is far better than rule-based automation. Thus, AI-driven test automation is the new normal for us to achieve the smaller to bigger business objectives.
Self-learning algorithms in AI:
It is possible to make the data an intellectual property itself because some algorithms work based on self-learning.
The answers are available in the data, all you need to apply Artificial Intelligence to produce the outcomes. When considering the best data at the competition level, AI helps you resolve complex business problems.
AI becomes adaptable through progressive learning algorithms. AI identifies the structure of the data and checks irregularities in it before becoming the algorithm as a predictor or a classifier.
Likewise, algorithms teach themselves about how to play a chess game. Similarly, AI teaches itself to recommend products online to customers.
Apart from that, there are several learning models in AI that can be defined as supervised, semi-supervised, unsupervised or reinforced.
For example, supervised models choose the external environment to play a role of a teacher in the AI algorithms. It learns functions with the use of external feedback and translates inputs to output observations.
On the other hand, semi-supervised learning models use a set of labeled and designed data. It tries to infer new attributes or labels on new data sets. At the same time, unsupervised models aim to learn a pattern of the input data and don’t require any external feedback.
Furthermore, reinforcement learning is also popular in the latest AI solution because it utilizes opposite dynamics from rewards to punishment to strengthen various kinds of knowledge.
In most cases, we can sell an individual application using AI. The products which we already use can integrate AI techniques to help people know about the new generation services. For example, Apple products add Siri as a new feature to get the number of answers in quick minutes.
From smart machines, bots, and conversational platforms to automation, one can combine the large amounts of data in it to introduce new features and technologies.
There are many more innovations we can enjoy in the age of AI. Another example is Robotic Process Automation, which is an AI application that one can choose to perform unit testing and remove flaky test cases.
Benefits of Using AI in Software Testing
AI in software testing aims to make testing more efficient and smarter. Both machine learning and AI use reasoning and problem-solving techniques to improve and simplify the processes of testing.
More and more companies are using AI in software testing because it saves a lot of time compared to manual testing and supports the teams while focusing on complicated tasks.
When making simple changes in the application, even test automation tools fail to give the right results because the conventional testing scenarios use a singular selector, which sometimes becomes the reason for test failures.
Hence, it is essential to choose AI and machine learning for test automation because it gives you more flexibility during the entire testing process.
While optimization and machine learning, many algorithms show their adaptive nature. During the test, when you add any custom feature in the application, these technologies will adapt to the new changes in real-time and help you work as per your preference.
AI is trying to take over our lives, yet it is our responsibility to confirm that such systems are resilient, compatible, fully-functional, and safe to use. Those who have done machine learning model training already know that testing is the foundation of AI projects' success.
Here, they don’t need to build an AI algorithm or worry about throwing or calling the data in it. One needs to conduct the validation testing to ensure that the training data can perform a good job.
You also need to conduct QA testing to ensure that the data and algorithms are taken into account with hyperparameter configuration data with its associated metadata and will help you obtain the predictive results. Before putting the AI models into operations, it is crucial to perform the specific type of testing.
Test automation tools powered by AI help developers, QA testers, and other teams meet their objectives related to gaining high-quality and releasing the apps faster.
Additionally, AI helps you maximize the scope and depth of tests. It can check the data tables, file contents, memory, internal program states and determine whether the program works as intended. With AI-based test automation, you can implement more than thousands of test cases in one go, which is honestly not possible through manual testing.
Connect With BugRaptors
BugRaptors is an independent software testing company and has many years of experience in QA and automation testing. Organizations such as Allego, Walmart, Salesforce, Ford, Verizon, Shatri Store, and many other global leaders rely on us for AI testing services. Whether you have a smart assistant, manufacturing robot, virtual travel booking agent, social media, inter-team chat tool, plagiarism checker, or using ridesharing apps like Lyft and Uber, come to us because we ensure to make your AI-device fully-functional and secure.