Artificial intelligence subverts four major methods of software testing

The use of artificial intelligence in software testing is becoming increasingly common, and for good reason. Although AI technology is currently mainly used for automation and improving convenience, it is gradually becoming smarter and more efficient every day. In software testing, artificial intelligence can be used to enhance many repetitive tasks. This both speeds up the process and improves accuracy.

Regression testing is an example. Every time a modification is made to the software code, regression testing is performed to ensure that the application still functions as expected. This is usually done manually. The entire process can be automated, with artificial intelligence automatically checking the application for issues after changing the code and performing maintenance at different intervals.

Four ways artificial intelligence subverts software testing

1. Regression test automation

Usually, manual regression testing requires testers and developers to spend a lot of time and energy. Every time the software code is changed, the resulting application must be tested. It ensures frequent communication between developers and testers or requires developers to take on more responsibilities in the absence of testers.

An artificial intelligence solution can automate this process and perform tests immediately after modifying the code, with little to no human intervention. With proper training, AI can consistently perform faster, more efficiently, and more accurately than manual testers.

In addition, developers can get test results and related data faster so that they can start making necessary fixes immediately. Or, if no issues are detected, they can move to other areas of development more quickly.

Adding another layer on top of this can make regression testing better over time as the AI ​​becomes smarter.

2. Early fault and bug detection

Ensuring that the software code and the application itself conform to standards and perform at the expected level is an important element of software testing. Therefore, a big part of testing is finding bugs and other software problems and fixing them.

In large projects, even the most skilled developers can miss syntax errors or coding errors. While modern programming tools can detect basic errors, natural language issues are often ignored until they are discovered at runtime. Artificial intelligence in software testing can detect errors and bugs by finding them.

Machine learning (ML) relies on natural language processing (NLP), training data, and pre-programmed scanning tools to locate potential issues and flag them for review. Even better, if the fix is ​​more obvious, the AI ​​can directly inject new code. It saves time, money, and a lot of headaches.

3. Productivity Improvement

By discovering faults and vulnerabilities early and conducting more beneficial regression tests, developers and testers can expect to achieve improvements in overall product efficiency and productivity. With the help of artificial intelligence and machine learning systems, many of the rote tasks associated with development can be accelerated, enhanced, or simply undertaken through automated solutions.

This is not unlike what we are seeing in other industries, where AI is increasingly used to power robotic process automation (RPA), intelligent automation (IA), and more .

For example, communication between teams and departments. It is usually done after application testing. Now it can be handled through automated solutions. Information can be shared instantly to all relevant parties without the need for relevant contacts to send it manually. The same benefits extend to nearly every process and solution involved in AI.

4. Self-healing code

Self-healing technology or self-healing code has been implemented, based on artificial intelligence and machine learning. Not only can AI identify and detect language issues faster than humans, but with the right resources, it can fix code faster than ever before, with early error and failure detection just the beginning.

AI solutions can actually inject code fixes for common problems, or they can directly fix more complex problems when they know what to look for and how to react. Even better, as we often see with machine learning, self-healing algorithms become more accurate and more representative of working code over time simply by ingesting more information.

If a developer’s misoperation causes a problem with the software, artificial intelligence will immediately take over and fix it. It saves developers and testers time in finding these issues and significantly reduces the time required for general troubleshooting. It can also flag common issues for later review to ensure errors don't reoccur and issues don't compound throughout the development phase.

Artificial Intelligence in Software Testing: Good Disruption is Coming

The field of software testing is likely to be disrupted by artificial intelligence as smart and efficient processes increase productivity. Artificial intelligence can almost completely automate manual tasks that take a lot of time and effort.

NLP tools can be used for early error detection, while self-healing code can also ensure that software applications continue to function normally at runtime. All of this reduces time spent troubleshooting and manual error detection.

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