Teams that are responsible for software QA testing might test and discover bugs in thousands of lines of code each day. Their goal is to help create an end product that works as expected, provides a great user experience, and is performance efficient. Traditional software testing methods often fail to work when the numbers of users increase, and they are often expensive and inefficient. Teams need a scalable, cost effective solution.
Further, as software functionality becomes more complicated, accurate testing is increasingly difficult to accomplish. At the same time, customer expectations are increasing. Testing teams are struggling to test things manually in a reasonable amount of time. Many companies are responding to this by ‘throwing more people at the problem’. Unfortunately, this is not a sustainable solution.
Software Testing Requires Modernization
Agile development practices and other modernization strategies have helped software developers meet the needs of modern users. Now, there is market demand for similar modernization where the subject is software testing.
The combination of artificial intelligence and machine learning can create an infrastructure where potential coding errors can be identified and fixed before they are even created. As ML functions analyze a significant number of data patterns, the more they learn. These learned patterns can then be used to better predict what inputs the software might receive.
Advantages of Implementing AI And Machine Learning
Automating The Software Testing Process
The point of using artificial intelligence and machine learning in software testing isn’t to remove human testers from the equation. Instead, it’s to pair human skill and instinct with technology to make the testing phase of the SDLC better. When software development firms use artificial intelligence professionals who are capable of applying their advanced solutions in cooperation with existing software testing processes, the testing becomes more thorough and error proof.
In addition to that, AI can add automation to the testing process. By reducing the amount of repetitive but necessary tasks testers can perform, AI allows testers to focus on more important aspects of testing. Also, this prevents the testing fatigue that often results from that kind of repetition. AI is also superior to most common testing automation tools.
AI algorithms can be trained to capture, recognize, and analyze large data sets that humans never could. This speeds up the testing process, reduces cost, and increases efficiency.
Here’s an example. Consider a traditional testing tool that isn’t able to discern the value or need of one test from another. AI can analyze the most recent changes, tests that have recently been run, and execute only the tests that are needed. Software developers are then able to focus on more strategic efforts.
Bug Detection And Removal
Bugs happen any time software development happens. This is a source of frustration for developers, testers, and end users. Machine learning algorithms can be used to make note of bad code, and identify bugs before they go into production. This means testing teams spend less time searching out well-hidden bugs. AI can even be used to help determine whether coding changes are needed to prevent potential errors.
Brainberry AI Software Professionals
We have team members who can apply their expertise to fully understand your business needs, and determine how AI software testing can be used to improve your software development processes. We’ll help you find the existing tools that will meet your needs, or custom develop an AI/ML solution for you.