When releasing software to the market, a lot of testing is necessary to ensure everything works perfectly. In the past, it could take years until a product could be tested enough to hit the market. For example, Windows 10 took two years to come out after its predecessor, and Apple often takes more than a year to update its iOS.
5 Ways AI Can Improve the Software Testing Process
- Generates test cases automatically.
- Increases test stability.
- Offers in-test assistance.
- Increased test coverage.
- Identifies issues automatically.
Thanks to AI, we now have technology that can significantly speed up the testing process allowing business owners to release their products in a matter of weeks instead of months, providing a potentially significant competitive advantage over competitors.
How AI Can Improve the Software Testing Process
As more and more people adopt technology, software releases have also become more frequent. To keep up with the competition and hold the spotlight on innovation, company owners have one goal: to release their product before anyone else does. To get ahead, they have traditionally employed automation to streamline their business process and release products faster.
The problem with traditional automation is that it requires a fair degree of human involvement. While AI is still primarily used in conjunction with human intelligence, setting this system up is no longer as complex. With its ability to generate test cases based on a vast data pool, AI testing software can help find bugs in the code without breaking too much of a sweat.
AI significantly improves the real-time software testing process by bringing automation and a significant data pool into the picture. During the testing process, it can offer assistance in the following aspects:
1. Generates Test Cases Automatically
Creating tests can be very daunting, whether you go through the manual or automatic route. Regardless of the scenario, you still have to write down each new test. AI-assisted testing software can generate those test cases automatically, taking away repetitive and time-consuming tasks. Maintenance is also handled more efficiently, as the AI systems can adapt to different scenarios when they are due.
2. Increases Test Stability
End-to-end tests done through traditional tools are not always completely reliable, as they have too many dependencies. Without the proper maintenance and modifications, a code can stop interacting correctly with third parties. AI testing systems make this part much easier, as they automatically update the testing codes in the manner that they are supposed to work. This prevents them from crashing and increases the overall stability of your test.
3. Offers In-Test Assistance
AI also provides in-test assistance during the test creation, mainly through the use of generative AI. Artificial intelligence systems are often self-learning and can improve the efficiency of a test by using your past experiences. It can also generate dynamic tests based on the feedback you have already received, continuously adapting the scenario to fit testing requirements. Eventually, this can make the test more comprehensive and responsive.
4. Increased Test Coverage
Automation has long since been used to offer better test coverage in comparison to the usual human route. However, since it still relies on human knowledge to work, its coverage only extends as much as you set it to, and there are plenty of limitations. On the contrary, AI tools like Testim, Kualitee or Selenium can tap into larger data pools, offering significantly more test coverage across different scenarios.
5. Identifies Issues Automatically
One of the greatest benefits of AI-assisted software testers is that it can detect these issues automatically. Think of AI like a second brain. Since they are self-learning systems, they can tap into their data pool and identify an issue within moments of appearing. This improves the testing success and prevents you from releasing a defective product to the market.
3 Long-Term Benefits of Using AI
Short, real-time benefits are not the only thing that makes AI a helpful asset in software testing. In the long term, such an addition can benefit the product and the overall company performance in various ways:
1. Reduced Company Costs
Traditional automation can help reduce the time needed to release a product, but engineers still need to set it up. This means you will have payments to make even though the test runs automatically. AI-assisted software testing will automatically set up those tests, reducing the human effort spent creating different codes. Adding that to the reduced maintenance costs that AI could cover, it can save quite a lot of money in the long run.
2. Shorter Time-To-Market
With AI’s assistance enabling you to optimize your resource utilization and find bugs much faster than usual, time-to-market is also significantly reduced. This allows you to reap the earnings from the sales faster than the typical schedule while enabling you to focus on the next project.
3. Improved Team Dynamics
When there is an issue with the software or the system used to test it, it throws the testing team into a state of disorganization. When only human intelligence is used, the test creation can only be done by coding professionals since it’s a skill that not everyone possesses.
AI made that easy, as it simplified that language for everyone to understand. Now, every member of the team can make their contribution when they feel like it. Since no one is left behind anymore, it significantly improves team dynamics, increasing productivity.
Challenges to Using AI for Software Testing
AI-backed tools can be beneficial for improving software testing within a certain company, but the technology has many faults. There are still certain risks that you could face while using this technology, including:
1. Potential for Errors
AI does have the potential to improve the accuracy of a test, but it’s still imperfect. Like humans, AI works only as well as it is trained. It can easily misinterpret the command if it sees a scenario that it has not been trained for or has an incomplete database. If not caught on time, this could lead to a false positive or negative.
2. AI Hallucinations
AI technologies were made to come up with outcomes based on possible data. If the data is incomplete or in a theoretical phase, it can lead to responses that are either false or have low possibility, otherwise known as AI hallucinations. For instance, when used in software testing, it can make the erroneous conclusion that a program works when there is no way it will.
3. Potential Cybersecurity Risks
Cybersecurity could also be at risk when using AI if the team is not careful. For instance, if the AI algorithm has a particular weakness and does not recognize specific malicious content, it can compromise the test integrity. Without robust security, this could put a user’s data at risk.
The good news is that AI is still a young technology that has seen plenty of improvement over time. If it is properly balanced with human intelligence, many of these risks could be easily offset.
Ultimately, you can still significantly cut through the production process by using AI to spot issues and create test cases within seconds. A faster release allows you to focus on a new project, which you can use to increase the company’s cash flow.
