Software testing is a crucial part of the product development process, and the reason for this is obvious. If the product released to the market has numerous bugs, it won’t attract users, while your company’s reputation will be affected. Even if you manage to fix the bugs and release an update very fast, precious time will be wasted, and your software may not be able to survive the severe competition. That’s why it is essential to test the software throughout the entire development process — in the end, you will get a fully functional product. Apart from this, it is much easier to find and fix bugs gradually, than to deal with all of them at the same time after the release. For example, to perform those tasks machine learning software testing could be very helpful.
So, software testing is a critically important task, but it can be rather complicated and time-consuming. However, it is possible to optimize the process. Keep reading to learn how to do this.
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How to Optimize Software Testing?
There are a lot of tips on how to optimize software testing. Here are some of them:
1) Start testing your software as early as possible
This is exactly what we talked about in the previous paragraph — it is easier to deal with problems one by one. Besides, bugs detected during the initial phase of the development process are also less expensive to deal with. Another advantage you should consider is that your team will get a better understanding of the product in the process of detecting and solving issues. It may even be possible to predict potential problems while working on the existing ones. So don’t wait until your software will reach the late stages of development — start testing early!
2) Document the testing in details
This rule applies to everything: the test plan, test strategy, and test cases. Keeping the testing process formal and documented will bring clarity about the product. First of all, it will be easier for your team members to understand each other. Secondly, sharing documentation with the client (if you are developing software for someone else) is a nice practice. Good documentation should be accurate and short. And, obviously, don’t forget to update it when necessary.
3) Use proper tools and testing models
There are more than enough testing tools and models to choose from, and each of them has its advantages and disadvantages. Don’t use the first ones you manage to find — consider your testing strategy and make a choice wisely. Such an approach can simplify the software testing process and cut expenses on it.
4) Establish priorities
Imagine that you detected an enormous quantity of bugs in your product. You start working on them and spend loads of time on fixing everything. It may be expensive, and the project itself may be delayed. What is worse, after you are done with fixing, you will have to test everything again and spend even more time and funds.
Software testing is essential, but it is not the only phase of the software development process. To complete the project on time, focus only on the most important defects. Those which don’t affect, for instance, user experience, can be fixed later. A clear testing strategy will help you to avoid over-testing, so pay attention to it when working on your documentation.
5) Hire a third-party machine learning or software testing team
Outsourcing certain tasks is a great idea, especially if your own testing team is too busy with other projects. In this way, you will save some time and probably even cut the costs — third-party teams offer their service at diverse prices. When choosing a partner, consider their language and location, as time difference and the language barrier can turn into a problem at some point. Apart from this, good communication is a must in case you decide to outsource the testing process. So stay in touch with your partner and keep your documentation clear and relevant.
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Machine Learning for Software Testing
All the above-mentioned tips can help a lot, but there is one more you should know about — you can use machine learning for software testing. Applying machine learning to software testing can bring you numerous benefits, and here are some of them:
Machine learning can help to minimize the manual efforts your team has to make in order to test the software. Tests have to be written, maintained, and interpreted, and all these procedures may take a lot of time. Machine learning can save both your time and effort. For instance, it can identify and remove outdated tests automatically, without human intervention.
Combining machine learning with predictive analytics, you will reach much more efficient results than applying traditional testing techniques. With the power of automation, algorithms can learn from the test data and, therefore, deliver intelligent insights, like malfunction patterns and predictions, information regarding typical defects and software stability, and so on.
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Machine learning helps to scale software testing projects
If you and your team use machine learning for software testing, there is no need to worry about the scale of the project. Do you need to test an application with more than a thousand users? That’s not a problem — automated testing can deal with as many of them as you need. With its help, it is possible to simulate a virtual set of users and test the product without any problems.
If you apply machine learning to your software testing processes, your product will enter the market faster than in the case of traditional methods. Apart from this, the quality of the software will be higher. This is possible thanks to advanced analytics, faster and more relevant predictions, better optimization, improved traceability, and real-time feedback. Besides, unlike humans, a machine learning algorithm won’t make mistakes.
Not only testers can benefit from using machine learning for software testing — developers can also enjoy the advantages. They can use shared automated tests to identify the issues even before getting in touch with the QA department. Tests can be run automatically when the code is changed. Then, a developer (or developers) gets a notification in the case of failure. This trick also saves time and ensures a better quality of the product.
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AI in Software Testing: A Few Useful Tools
AI is a great choice for software testing. However, there are a lot of AI-based automation tools, and it can be pretty complicated to decide which of them to use. To make your life easier, we prepared a list of popular AI tools for software testing. Take a closer look:
With this tool, it is possible to generate test cases on the basis of user behavior. Appvance ensures comprehensive coverage, while regression test suites are very easy to update. Apart from this, the Appvance website offers video tutorials and user documentation, which can be useful to you. Appvance also allows continuous testing, and free evaluation is available. Feel free to try it in case you are interested in using the Appvance AI tool for software testing.
Functionize promises 90% faster test creation and 6 times better productivity. And these numbers are not the only advantage of this AI tool for software testing. With Functionalize, your team can write tests in English, and then they will be transformed into functional autonomous tests. Besides, the tool automatically generates new test cases based on how users interact with your product. The tests are also autonomously updated, so you will be able to forget about test maintenance.
In order available tools on the market are not sufficient you are able to build your own Machine Learning system for software testing. With the help of the external Data Science consulting team, you are able to do it according to industry standards and use their prior experience to build systems for your needs.
Stay in touch
So, now you know how artificial intelligence and machine learning can be used for software testing. However, if you still have any questions (for instance, regarding how exactly machine learning can be used in software testing) or simply want to share your ideas, don’t hesitate to get in touch with us. We are always happy to help you with your project.