Source: Innovecture
AI will not turn software testers and engineers into Tony Stark overnight.
But it does push us further down that path than we have ever been in the industry.
With the help of his trusty AI, Jarvis, Mr. Stark can trim down software and hardware testing times to mere minutes, all while boosting the efficiency and safety of his Iron Man suit, to the endless envy of the US military-industrial complex.
Current AI capabilities are not yet at the level of the fictitious Jarvis, but testers armed with AI are still capable of some exciting things in the world of software testing which we are going to explore in this article.
Let’s start at the beginning.
Why Is Software Testing Crucial?
Just like the name implies, software testing is the process of verifying or testing the quality, performance, and functionality of software before launching or releasing it to the public.
The reality is a lot harder than it sounds. Software testing is usually a complex multi-step process requiring time, effort, and resources to accomplish properly.
Testers work with the software either manually or by executing test scripts to uncover existing errors or bugs that might prevent the software from running as expected.
But that’s not all.
Software testers also have to make sure that the software solution makes business sense as well, meeting any needs that may be required by a client.
As you can imagine, software testing is a crucial part of the software development process. Without it, you would see many more bug-riddled apps that diminish your bottom line and add to your frustration.
And you can expect the process to keep getting more complicated and demanding, with an ever-decreasing margin for error, as applications and business demands become more complex
It’s for this reason that many developers are turning to AI for better performance and results today.
AI Is Driving Evolution in Software Testing
To be honest, we are still in the nascent stages of AI development and deployment.
In a few years or decades, AI in general will be unrecognizable, and all of what excites us today will seem like the Stone Age–barely worthy of consideration other than from historical perspectives.
And yet, even at this early stage, AI is transforming the way we do software testing.
Thanks to artificial intelligence, natural language processing, machine learning, and other algorithms, AI-driven tools are being used to quickly identify critical defects, accelerate test cycles, and optimize the scope of testing.
This marriage between AI and software testing so far has been made in heaven, endowing teams with the capabilities necessary to deliver products faster without compromising on quality.
From innovative ideation to efficient test case scenarios to rapid and effective automation, AI is changing the game even at this early stage, and reshaping the software development lifecycle.
We see this in several ways:
How the Best Software Testers Are Leveraging AI
There are three key areas in which developers and startups are taking advantage of the AI revolution that is sweeping across the industry:
- AI-Driven Automation Testing:This involves AI systems that are created specifically for testing software and enabling cutting-edge automation.
- Testing AI Systems: This consists in coming up with effective methods to comprehensively test AI testing tools, leading to greater reliability.
- Self-Testing Systems: This involves building and deploying “self-healing” software systems that are capable of self-testing and adapting accordingly for greater performance.
Today, many AI test bot offerings help users in test generation, testing, discovery automation, and failure detection. Their reliance on features like neural networks, decision tree learning, and reinforcement learning makes them far more effective than traditional tools of software testing.
Benefits of AI Software Testing to Businesses
At the end of the day, convenience and greater efficiency are not the only reasons why software developers all over the world are choosing to integrate AI into their software testing processes.
Other reasons for this trend include:
1.Greater Test Accuracy
With AI, developers can design testing processes that eliminate human biases and errors, leading to more reliable results. Some businesses are already finding great success in using AI-powered test bots to single out subtle defects that would otherwise have been missed in a manual testing process.
2.Greater Speed and Efficiency
AI is capable of analyzing huge amounts of data, as well as executing test cases, at staggering speeds that outstrip human capabilities. All these make AI integration perfect for faster test cycles, quicker troubleshooting, and accelerated launches.
3.Scale
The increased speed and capabilities of AI make it easier for developers to meet the increasingly complex demands of today’s software landscape. Thus, with AI-driven software testing, businesses can handle a large number of test cases and requests for different platforms.
4.Predictive Analysis
AI is adept at analyzing historical data to predict potential issues and risks in the future. This endows testing teams with the ability to be proactive in their testing by addressing potential faults in a software solution before they reach the final end-user. In this regard, AI can save costs, effort, and time that could have been spent on post-release bug fixes.
5.Adaptive Testing
With AI, testers can dynamically adjust strategies and methods based on real-time feedback, user data, and system behavior. Unprecedented adaptability like this enables software testing teams to become agile and finetune their response to changing requirements and different environments.
6.Continuous Testing
AI-based testing seamlessly integrates into the continuous integration and continuous delivery (CI/CD) pipeline, allowing for efficient continuous testing throughout the development process. Without this AI functionality, software testers usually struggle to keep up with the constant demands of continuous integration and continuous delivery
7.Anomaly Detection
AI can rapidly collect data and learn the regular behavior of an application and its users–making it easy to quickly realize deviations from the norm. Once these abnormal patterns are identified, the development team can quickly react to the situation.
8.Test Automation
AI-powered testing tools can automate test cases, thus eliminating manual efforts and speeding up testing cycles. These algorithms accomplish this by identifying and automating repetitive tasks, allowing for faster and more comprehensive testing.
9.Data-Driven Decision Making
Developers can leverage the data-gathering and analyzing capabilities of AI, to make data-driven decisions at every stage of the software development lifecycle. This ensures results that are more concrete than when decisions are made based on mere assumptions.
10.Self-Healing Tests
AI testing tools can adapt to modifications and changes–such as UI modifications–by automatically adjusting scripts to suit the new parameters. This ensures reliability and reduces the need for costly maintenance efforts.
Conclusion
AI has revolutionized the way software developers and testers approach software development and testing. And, while there is still a long way to go before we can arrive at a point where humans are no longer an active part of software testing like we see in the movies, there are already enough benefits to integrating AI into software testing to make it a wise decision for any business today. Don’t hesitate to reach out if you would like to learn more about AI and user testing.
