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How is AI Transforming the Landscape of Software Testing?

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How is AI Transforming the Landscape of Software Testing?​

The QA space is constantly evolving with high-end technological innovations. One of the most dominant trends that is reshaping software development and testing is artificial intelligence and machine learning. The blog explores how AI is influencing software testing practices. Give it a read.

How is Artificial Intelligence Redefining Software Testing?

1. Predictive Analytics

In today’s competitive digital landscape, customer-centricity lies at the heart of product development. It’s important to understand the customers’ needs to stay ahead of the competition. This is where predictive analytics plays a crucial role.

With time and technological advancements, AI has acquired the ability to process collective data and make smart predictions. Machine Learning (ML) is an important aspect of AI that leverages pattern recognition technology. It finds out patterns in the provided data and uses these to predict future trends.

Further, AI can predict and equip the application for real-time scenarios. Here’s a use case for better understanding. In an eCommerce app, people may add items to the cart either from the product details page or from the homepage. AI can recommend that the checkout process should be tested from both pages.

AI is constantly evolving by processing more and more data sets & behaviour patterns and thereby enhancing its capabilities. Collectively, AI and ML are improving the efficacy of testing techniques and contributing to enhancing user experience.

ls a REST endpoint as a java interface and makes it super easy to get Responses from Web APIs and parse them into Plain Old Java Objects (POJOs).

2. Reliable Locator Strategies

Forbes highlights in AI in Software Testing “Development teams no longer have the luxury of time they had when monthly product launches were the gold standard. Today’s enterprises demand weekly releases and updates that trickle in even more frequently.”

In this time-constrained environment, the marketplace demands efficient automated testing tools, especially during the final stages of product development. While human intervention and manual testing are not to be subsided anytime soon; automated test solutions are making the process easier.

Platforms like Testim, Testsigma, Test.AI, etc. are some of the providers of automated testing solutions. Artificial intelligence powers these platforms in real-time, analyzes the DOM objects of a page, and extracts the objects along with their properties.

AI decides the best locator strategy in order to locate a particular element based on the analysis. That’s why even if a developer changes the attribute of any element, the test continues to run. This leads to more stable tests. AI also helps in localization testing and makes sure that the behaviour of the product corresponds with the specific region.

3. Visual Validation

AI powers visual testing and enables test engineers to leverage image-based learning and screen comparisons for testing the aesthetics and functionality of any application. There are several ML-based visual validation tools that help in detecting intricate UI anomalies.

These tools help in recognizing dynamic UI controls irrespective of their shape and size and analyse these at a pixel level. Thus these make sure that all the visual elements are engaging and highly functional.

Applitools is one such visual testing and monitoring tool. It can be easily integrated with all modern test frameworks, build servers, source control, and collaboration tools.

Percy by BrowserStack is yet another all-inclusive visual review platform that is equipped with different features such as pixel-by-pixel differences, snapshot stabilization, and responsive differences. The tool has parallelization capabilities and ensures high-speed, cross-browser rendering.

Such visual AI tools help test engineering teams reduce the amount of test code they rewrite, the time they spend running & fixing tests, and time spent analyzing the results.

4. Enhanced test coverage

AI helps in increasing test coverage as it checks the file content, data tables, internal programme states, and memories. AI in software testing can perform different test cases in a single test, providing a wide range of coverage. It also helps in analysing if the programme works as expected and delivers effective test coverage.

5. Specify the Areas of Testing & Speed Up Time-to-Market

AI smart predictions can recommend which aspects of the product need to be tested. These recommendations allow the test engineers to pay attention to the areas that could be overlooked otherwise. Thus, AI-enabled smart recommendations help in reducing overall testing time.

Also, AI-driven tests facilitate continuous testing. Continuous testing is the process of conducting automated tests as part of the software delivery pipeline where changes are constantly tested and the delivery is streamlined. Thus, the products are released faster and businesses can go early to market.

Is AI Replacing Human Test Engineers?

AI has undoubtedly brought groundbreaking changes to software test engineering. By giving test engineers a break from scripting everything manually, AI helps in speeding up the entire process. However, human intervention cannot be entirely replaced. It’s important to consider an AI platform as a tool to power up software testing, but not the ultimate.

Simon Steward, the creator of Selenium WebDriver, asserts in an interview with Joe Colantonio: “You don’t want to run (automate) everything because you don’t know whether or not it works. We should have precision at this point. We shouldn’t be doing like blunt force trauma to our codebases.”

Based on the end users’ requirements, the test engineers need to adjust the usage of AI. Manual testing should be incorporated into the testing infrastructure as and when required.

This is where a software test consulting company can come to help. The consultants and testing teams can suggest and execute varied types of tests and meet the business requirements.

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