Mobile applications surround us at every step. Therefore, when releasing a new app, you need to make efforts to develop a functional and successful product. Thorough testing and examination of applications is a guarantee of their smooth performance and popularity.
However, achieving high verification results can be difficult with a traditional approach. The solution may be the implementation of artificial intelligence and full or partial automation of the process. What are the advantages of the method and how to prepare an artificial intelligence model, you will learn from this article.
Benefits of Using AI for Mobile App Testing
TestFort believes that mobile app testing is of great importance for traditional AI testing and machine learning practices. However, the classic approach has its drawbacks. It is slow, laborious, and often expensive. On the other hand, automating the process with the help of artificial intelligence speeds up testing and increases its efficiency.
To involve a new method in the current workflow, you need to use high-quality AI test automation tools. They combine guided algorithms and machine learning, guaranteeing high testing efficiency.
Using them, you will get:
- Speeding up the testing process. Automated tests save you time and do not break the deadline.
- Increasing the accuracy of results. AI tests the product objectively and often deeper than a person does.
- Saving money. Implementing new tools requires investment but is more economical in the long run.
- Increasing test coverage. Machine learning can test the product in those areas that are most necessary and impossible to examine with the traditional approach.
- Round-the-clock testing. AI doesn’t need a break, so it can test your app even outside of work hours.
Using AI in QA automation allows you to identify patterns that can predict potential problems in your app. It makes the testing process fast and efficient.
Best Practices for Training AI Models
To prepare a model for use in the testing process, you should follow a clear and concise procedure. Here are the main steps that you are recommended to take.
Prepare High-Quality Data
Artificial intelligence cannot learn on its own. You need to prepare a database that will become the basis of the model’s knowledge. Its list will include previously used test cases, bug reports, performance indicators, user clickstream analysis, etc.
Choose a Suitable Training Algorithm
There are two main approaches that you can use. A supervised algorithm involves training the model on labeled data. The input and output labels will predict the future outcome of newly analyzed data. An unsupervised algorithm is slightly different. It does not involve the use of response labels and does not contain specific instructions.
Train and Test the Model
With high-quality data as a basis and a suitable algorithm at hand, you can proceed to train the model. You will need to distribute the data, optimize the performance metrics of the model, and perform cross-validation.
Strive for Continuous Learning
To ensure that your AI-based automation performs well, continuously track the metrics you get, collect them in reports, and feed them back into the model. This will help improve the productivity and efficiency of the process.
Implement More Modern Approaches
Use self-healing scenarios. They will help the model update test cases according to every change in the application.
Some Tips for Improving Your AI Experience
Implementing machine learning can take time and effort. Here are a few tips to help you adapt to the new approach:
- Prioritize automated test cases. You won’t be able to automate the entire process or use AI to test all of your app’s features right away. Choose the test cases that you need to improve first.
- Select proven AI tools. There are many tools you may choose, but not all of them are effective. Determine the most suitable and proven ones.
- Combine different approaches. Using AI cannot completely replace manual testing and vice versa. Implement different approaches and tools to achieve better results.
- Use NLP (Natural Language Processing). It can greatly help in creating better test cases.
- Increase your knowledge. Machine learning tools are constantly improving and evolving. To keep up with trends, improve your team’s knowledge in the area and add new tools.
You can implement a new approach or test a mobile application qualitatively, safely, and efficiently with TestFort. The company specializes in comprehensive mobile application testing and has over 200 physical devices for product examination.
Its many years of experience in the market guarantee accurate and fast testing results. The company is one of the industry leaders that has a large team of professional QA engineers and several hundred successfully completed projects. Contact it right now to ensure thorough app verification.