The New Era of Smart Test Automation with AI

The New Era of Smart Test Automation with AI

Automation empire: Digital transformation is the new normal, and digital maturity demands quick adoption of DevOps and Agile principles. Today, everyone in the market desires to achieve efficiency, quality, and business agility for their organizations, such as delivering exceptional customer service and satisfaction, rapidly introducing new products and services to customers, giving an instant response to changing market demands, having a corporate commitment to the environment, and considering shareholder value as a leading KPI (Key Performance Indicator).

Moreover, Artificial Intelligence (AI) and Machine Learning are becoming successful in creating so much buzz in multiple sectors and encourage companies to make applications with it or run online businesses using these technologies.

All over the internet, the popularity of using software testing and test automation is growing at a faster pace. Software testing companies start using test automation more with manual approaches. Automation empire

As per a report of Markets and Markets, the worldwide automation testing market size is projected to grow from $12.6 billion in 2019 to $28.8 billion by 2024. The growth seems phenomenal at the CAGR (Compound Annual Growth Rate) of 18% during the forecast period. Automation empire

One reason for the automation testing market’s growth is the increasing adoption of Agile and DevOps, CI/CD implementation & continuous testing, which are used as effective strategies for testing real-world software. As it helps improve quality and test coverage and allows companies to give speedy delivery of the product and service into the market.

Now, if you are thinking that everything is going good with test automation and the newest trends of software testing, then why is it needed to embrace AI (Artificial Intelligence) in testing processes?

Importance of AI-Based Testing

With AI-based testing, it is possible to obtain favorable outcomes using the several phases/stages of software testing, especially test automation. There have been numerous frameworks and various tools that are mainly designed to resolve different kinds of test automation issues.

Still, one issue that has been a significant challenge today is the factor of “maintenance”. This is because we utilize only one attribute of an entity with static locators to define it on a page uniquely. When this changes, the test breaks & half of the testers’ time get wasted while troubleshooting the problems.

As per the research, a maximum of 30-40% of the time the testers spend on the maintenance of tests. You may know that the cost of opportunity is directly linked to this effort. Thus, It is good that testers greatly use their time to discover the application, like what it is, what features it includes, what problems the users often face while running the app. After exploring all things in the application, they share the information in real-time with stakeholders to make the right decisions for using the product.

With AI testing services, you can use dynamic locators that are the concept of using multiple attributes of an element to find it on the website, rather than using the single locators which we discussed above how it breaks the tests when one attribute changes and how it creates the problems in the testing process. But, when we use the dynamic locators in AI-based testing, it allows us to locate the element with the help of attributes, which is already extracted from the AI’s domain object models. Automation empire

Artificial Intelligence (AI) can now automate the testing processes and allow businesses to improve their strategies. With the aid of pattern recognition technology or machine learning that is used to identify patterns via algorithms and forecast the trends that may come in the future, AI is proving useful for test automation and software testing. With pattern recognition, it becomes possible for machines to study and predict what can happen in the future. Based on that, the data is applied and used for making better decisions.

Artificial intelligence even allows us to connect the production applications to the testing cycle. This means you can create effective tests based on the actual flow managed by the user in production. The benefit of using AI is finding and observing repeated steps and supporting clustering for re-using the components like login & logout scenarios in your tests. Due to its self-healing mechanism, you can conduct as many tests and make it easier for AI to recognize flaky tests, optimize the problems, and fix the testing issues faster. Automation empire

The test automation field is suitable for technical testers, but this case is now solved with AI (artificial intelligence) as it permits non-technical users to collaborate in test automation. There is no longer a need to be scared of complex coding languages and the latest technologies because it helps connect or build trust with the technical expertise, development teams before and after the execution of tests and make workers’ jobs easier.

What Exactly is Artificial Intelligence?

In the layman language, AI has the power of machines to perform specific tasks and activities that we can refer to as ‘intelligent’. It is an intelligent device that allows you to do tasks and gives ease to understand its environment for fulfilling the unique business goals as best as possible.

In software testing, AI focuses on collecting information about the quality and the system, which faces major risks. It provides massive support during the testing of the software after observing the collected data and the information system. Further, one can use this collected data in reports and make it comfortable to implement the test cases until better outcomes don’t produce.

Artificial Intelligence (AI) Systems are Much More Different from Conventional Software Systems:

Accuracy

The software’s or any app’s accuracy relies on the skill sets and experience of programmers. The application or software can become bug-free or more successful if developers put much effort and use techniques to produce output as per the required design. Automation empire

Characteristics

Software is pre-programmed or deterministic to obtain a specific output based on the given set of inputs. However, both ML (machine learning) & AI (Artificial Intelligence) are non-deterministic, meaning the algorithms will act differently whenever it is running with different scenarios.

Bugs/Defects

If the software is found with more defects, the only credit goes to poorly-used coded functions or human intelligence. In contrast, AI systems include self-healing properties to resume operations after managing exceptions or errors.

Programming

All software functions are performed using if-else conditional statements and looping statements to convert the input data into output data. On the flip side, for AI, we feed the combination of different inputs and outputs in machines so that AI can learn and describe the function accordingly.

How to Use AI in Test Automation?

First of all, it is vital to know the usage of this type of technology. For this, you can analyze a few possible scenarios in which you need to use artificial intelligence. Every software has a different nature that may require different testing types such as functional testing and performance testing to meet the various business needs. This process can be time-consuming because many programs need to produce the possible outcome from a test. On the other hand, artificial intelligence and machine learning aim to help computers in learning on their own to a particular extent so that they can forecast without the tech professional that monitors every keystroke. Moreover, machine learning allows computers to do maximum work without any hassle.

Know the AI-driven Test Automation Impact on Software Testing

AI gives numerous advantages to the following areas of software testing:

Unit Testing

RPA or robotic process automation is the widely popular application of AI that testers use, or several tools of RPA are available for the software testing companies to minimize flaky test cases while performing unit testing. Furthermore, RPA tools like BluePrism & UIPath are useful for the maintenance of unit test scripts.

UI Testing

It is an excellent approach to use AI-powered tools whenever you want to automate your UI testing rather than implementing it manually. With AI-powered UI testing, you can get more accuracy. You can detect the parameters like combinations of colours, GUI size difference, which is not an easy concept when considering manual testing.

ML/AI

AI & ML based testing tools become more robust for understanding the intended usage of an application. These tools do that by studying the application, and they obtain valuable information about the application. Consequently, they utilize this data to build test cases where the anticipated outcomes are displayed.

API Testing

Always find the root cause of a defect or bug when you test any application. API testing allows you to get a more in-depth insight into the application. With AI-based tools, it is much simpler to transform manual UI tests into automated API tests, and very less knowledge or skills are required to get the API testing done. Therefore, the best relief companies can now get to build a sustainable strategy for API testing. 

Testing Via Visual Validation

One needs visual validation to execute the image-based tests. Such types of testing become easier when using the machine learning capabilities of AI. In this case, automated visual validation tools like BackstopJS, GreenOnion, Visual Regression Tracker, AyeSpy, Needle tools give a remarkable experience for performing regression testing.

Smart Test Automation with Latest AI Technology: Embracing Change is Essential

In the age of Agile, test automation is a crucial capability for expediting faster product iterations. Whether you are a tester or an IT leader in an organization, you should keep yourself ready for embracing new change and use smart test automation with AI to transform the ‘impossible’ to ‘possible’ in software testing.

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