Does this sound familiar? Your team creates software, tests it rigorously for bugs, and releases it in the market. And just when you are about to take a sigh of relief, BOOM, it’s time to release the next update or feature. It’s time to go through the entire testing procedure all over again. It’s time for the same frustrating days that you thought were over. Well, at least for some time.
Software testing can be a pain, and if you are a large enterprise with software that has complicated architecture, it could easily become a reason for your sleepless nights. We understand this because, as a software testing company in the USA, we faced a similar problem when we were just getting started. But for every problem, there is a solution.
For this problem, the solution is Artificial Intelligence and Machine Learning. And we can say that with confidence because we have been using it for many years, and it has served us well.
In fact, we reduced the overall manual testing time by 80% on every release for a client with automation testing.
In this article, we will discuss the problems the software testing industry is currently facing and how AI can help overcome them. Keep an eye out for the issues you might be dealing with too.
Challenges Faced by the Software Testing Industry (and how AI can solve it)
1. Tight Deadlines
In today’s cut-throat world, if you don’t keep your customers engaged and interested, you’ll lose them quickly to your competition. To avoid that, you must release new features and updates quickly and keep your audience on their toes for the next update. On top of that, the new feature must work flawlessly, and the entire architecture of the software should be able to sustain the weight of new users.
We understand that’s a lot. Because that means you are constantly on a deadline to deliver results. And you can make no mistake with testing. Also, there are so many tools and testing suites to choose from, it could become a nightmare. That is the most massive challenge that organizations are facing today.
The AI development services make it easier for testers to understand which test cases they must execute in each test cycle. The best thing about it is that it automatically gathers data from previous application test executions, allowing it to easily detect changes in the code. It also suggests test suites based on the application’s risk, which are defined by taking several factors into account.
That includes (to name a few)
- Identifying the most frequently used parts of the application
- Tests with a high failure or complexity
- The probability that a cyber-attack can occur.
This method will help detect defects and bugs early on. Also, it will prove useful in running test cases that might get affected at the start of a test cycle. That will facilitate quick feedback from delivery teams and ensure that software testing service providers find solutions when they discover any defects.
As technology gets more advanced, smartphone companies are able to reduce the prices of mobile phones. The reduced cost and improved internet connectivity have prompted more people to get smartphones. In fact, the number of active smartphone subscriptions has crossed 6 billion worldwide.
That means people will access apps and websites simultaneously from thousands of different combinations of platforms, devices, and browsers. Testing software for such diverse environments can lead to low accuracy, which could be a problem for your brand image.
Apart from that, there are many repetitive tasks that testers have to perform manually. That could also increase the chances of human error in QA testing.
The best software testing company will employ automation to reduce the workload of testers by taking care of repetitive tasks. When AI is used to power automation, it performs repetitive tasks while testing teams get the freedom to complete more productive work. As a result, it saves time and improves accuracy for achieving quality results.
With the use of virtual reality and augmented reality on the rise in applications, the scope of testing also needs to increase accordingly. To be robust and flawless, an application must be thoroughly tested in a variety of environments. For complex applications, your testing team must ensure that every line of code is subjected to rigorous testing. Therefore, this process might take a long time.
However, you don’t have time since your users expect you to deliver updates timely. That puts undue pressure on the testing team. Due to this, QAs may fail to prioritize key test cases, resulting in inadequate test coverage. This leads to insufficient testing and allows bugs to enter production.
Artificial Intelligence technology is effective at expanding the scope of test cases. It monitors the app’s memory, internal programs, etc., to ensure that the functionality is up to the mark. Furthermore, AI-powered test automation tools can execute several test cases in a single test run. This AI capability significantly speeds up and increases test coverage.
4. Script Execution Issue
With complex apps and dynamic websites, the script execution could be an issue. Longer load time, delayed response, and poor scalability are a few problems that could arise. In the case of dynamic content, the locators that testers use to identify the elements of a web page may fail to interact with them. That will defeat the purpose of automated testing and will give poor results.
When using the latest machine learning technologies, there is no need to automate test scripts because AI will execute them automatically. It also makes test cases more stable, ensuring there is no change when the locators are modified.
5. Increased Cost
Due to the above-mentioned problems, if a bug or glitch gets passed on to the final and live product, it could become a financial nightmare for you. It would take a lot of time and resources to identify, resolve, and verify the functionality of the app. You may even have to hire additional testers to make your app glitch-free again. That is just going to shoot your budget through the roof.
As we discussed above, when bugs or errors in the application are not resolved in the beginning, they can become costly. And custom machine learning solutions help with it, which significantly reduces costs.
Furthermore, AI is even more efficient at sorting files, detecting errors, and scanning code in less time. That reduces manual labor and human error, giving more accurate results at a lower cost. Also, you can hire a software testing service team for the same.
To offer the best experience to your customers, it is important to experiment with new technologies and lead innovation. But this is only possible if you test your solutions extensively, including every scenario and environment possible. As a leading software testing company, Narola Infotech can help you fulfill all your testing-related requirements.
For 17 years, we have tried and tested different methods and strategies. And we have successfully applied those solutions for multiple clients. Whatever your issue is, we are sure we must have come across it before and have an answer ready for you. All you need to do is contact us, and our expert will be with you right away!