As artificial intelligence and computer science gets closer to becoming self-sufficient, and the future of artificially intelligent machines is coming closer, talk of test automation is turning up a lot. Test automation (or TA for short) is a way to evaluate the effectiveness of a machine learning system by letting the machine run various tests (usually simple ones) over again, to see how it improves over time. Some say that the days of TA are numbered, but I am not so sure. In this article I will show why I think it is still a relevant term even for those whose interest lies in machine learning and some of the benefits of its use.
Testing automation is simply the act of applying existing software test cases to the new test suite that may be generated from the existing test cases. Generally, existing software test cases are simply re-written in a more readable style, with shorter and cleaner code, so that the machine learning applications can easily understand them. The main advantage of this approach is that it helps the developers who wrote the software to get familiar with the test environment and therefore increases their ability to provide quality services. Another big advantage of using it as a part of your software testing suite is that it helps you make the migration easy, because you only need to change one line in the API call instead of rewriting the whole test suite. Since it uses the exact same API as your existing tests, all you need to do is replace the old API call with the new one.
Now let us discuss the main types of in-house ai automation. The two most common types are: internally developed and externally developed. The former uses source codes from the vendor and implements the same functionality; the latter uses a different language that compiles down to the same functionality as the vendor’s own language. However, both solutions have their pros and cons.
An externally developed system can cost more since you have to pay for the programming team, and the time needed to hire them and train them. But on the other hand, you will be saving a lot of money since you do not need to buy software from the vendor. For example, if you have to purchase 10-API applications for an IT project, then you will need to purchase those 10 API applications. But if you use an in-house developed AI Test Suite, you will just buy the software and the test automation tool, and you are done. Since the test automation tool will take care of most of the repetitive tasks, you will spend less time with programming and you will spend more time with actually running the test cases.
As you can see, AI business automation solutions are actually quite similar to what you already have at your workplace. Your employees already have an AI eLearning system integrated with their applications (for example, SAP), so there is really nothing new for you to do. However, the biggest advantage of using an AI eLearning system is the fact that your employees will become more knowledgeable about the technologies, and they will feel more comfortable using the automation system itself. The overall impact of the business automation system will be higher productivity and efficiency – and all these are possible thanks to deep learning.
The second big advantage is the ease with which you can create test cases. In traditional machine learning methods, you will need to create test cases manually – and you must be able to generate test data very accurately. But thanks to the artificial intelligence in your AIs, you can generate the test data so accurately and quickly that it becomes just another day in the office. You do not need to wait for weeks or months for the data to be generated – in fact, you can set up the test cases to occur instantaneously. This means that you will not lose any time when it comes to generating quality test data and you will not lose any time when you have to analyze the test data afterwards.
AI UML and MML scopes are the most commonly used tools for high-performance artificial intelligence systems. They allow you to create test code that will run on your UML or MML robot and generate results in real time. But these tools also allow you to create test cases and evaluate the code coverage of your product or service with the metrics from your software. The result is that even before you launch your product or service on the market, you will already be able to quantify its success based on the test results that you have generated using your custom AI system. So you do not need to wait for months or years in order to measure the performance of your product or service.
In conclusion, we see that there are three big advantages of using AIs in artificial intelligence development. First, they reduce the time needed for training your staff, they eliminate human errors that are usually committed during manual analysis, and they provide results in real time. But before you decide whether your company needs to invest in these types of tools, you need to understand how they work and what they can do for you. If you want to discover whether your business can benefit from AIs, discuss your plans with a data science consultant today.
