How Artificial Intelligence Impacts Software Testing

How AI is bringing change to the software testing practice

Artificial Intelligence is penetrating into multiple functions performed by the software industry. In software testing, the technology holds the potential to be a game-changer. Imagine the capability of your software to test and diagnose itself and make self-corrections! This will lead to huge savings on your resources. With this in mind, let’s try and understand exactly how AI will impact the traditional way of software testing.

Before we proceed, let’s get one thing clear – Do we really need AI in software testing?

Do We Really Need AI in Software Testing?

Software testing came into existence as a result of the evolution of development methodologies. It fed the need for robust, error-free software products. Testing was a laborious task for sure. However, automating software testing required traceability and versioning, both of which were critical and needed careful consideration. Something was needed to resolve this.

As businesses move towards digital transformation and the software market continues to grow, businesses expect a real-time risk assessment across all stages of the software delivery cycle. AI in software testing is the right response to these challenges. AI can develop error-free applications while enabling greater automation in software testing. This helps meet the expanded, critical demands for testing. It improves the quality of engineering and reduces testing time allowing the tester to focus on more important things. The verdict is clear then – We Really Need AI for Software Testing!

Five Impressive Ways AI Impacts Software Testing

1. Improves object application categorization

AI is widely used in object application categorization. When tools and testers are created, unique pre-train controls can be created. Once the hierarchy of the controls is categorized, testers can create a technical map to obtain labels for the different controls.

In the near future, AI will become capable of observing users perform exploratory testing on the testing site. And once user behavior is assessed, it can assign, monitor, and categorize the risk preference.

2. Automation of test case writing

Gone are the days of web crawlers. As automation is picking momentum, AI tools have become capable of learning business usage scenarios of test applications.

They can automatically collect insightful data such as HTML pages, screenshots and page loading time and eventually train ML models for expected patterns of the app. And as soon as they are executed, any variations are marked as potential issues. This makes it easier for the tester to find and validate differences and fix issues.

3. Enhanced accuracy

To date, source analysis requires human resources to accomplish the task. Unfortunately, because of the enormity of the data, even the best experts could overlook, or miss out on observing certain critical defects. Human error and the tendency to lose focus further impairs the experts involved in software testing. It can be disastrous if bugs caused by these errors are caught by consumers before project stakeholders. Product positioning and brand reputation can be jeopardized.

Thankfully, AI can teach systems to learn source analysis and, in the future, apply this acquired knowledge. This ensures that testers have greatly enhanced accuracy. It eliminates the probability of human error and also shortens the time to run a test and increases the possibility of finding defects or bugs.

4. Automation without the user interface

AI-based techniques can be applied for non-functional tests such as performance, security and unit integration. It can also be applied on various application logs which assists in developing auto-scaling capabilities such as bug prediction.

AI algorithms can enhance UI testing, predict the next test, determine the outcomes for subjective and complex tests and much more. In other words, AI could increase the overall test coverage while it increases the depth and scope of the test itself.

5. Reduces cost and decreases time to market

The need for manually repeating a test is time-consuming and extremely expensive. But with AI, such tests can be automated to repeat several times over. Each time the software test is repeated automatically, the source code gets modified to correct any bugs. This eliminates the additional cost of repeating the test and increases the speed of the test from days to hours, which in turn saves more money.

Allow AI to Revolutionize your Business

AI has proven to have a significant impact on software testing with its benefits ranging from optimization to extraordinary savings. It enables testers to move beyond the traditional route and dive toward precision-based testing processes. This can prove invaluable to your business. To find out how you can make this happen for your business, contact us.

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About the Author

Sreejith

I have been programming since 2000, and professionally since 2007. I currently lead the Open Source team at Fingent as we work on different technology stacks, ranging from the "boring"(read tried and trusted) to the bleeding edge. I like building, tinkering with and breaking things, not necessarily in that order.