Testing New AI Applications is Crucial Before Bringing them to Market

AI software applications are becoming more sophisticated, but they still need to be tested before being brought to market.

test your AI algorithms before bringing them to market
Shutterstock Photo License - Blue Planet Studio

AI technology has changed many aspects of our lives. However, it is not without its own limits and challenges. A recent news article written by Kevin Dallas in The Hill talked about some of the ethical issues associated with AI.

The truth is that AI technology must be properly designed and used responsibly. This includes developing AI software. Creating a new software application with complex AI algorithms is a very time and resource-intensive process. You are going to need to do your due diligence and make sure that you get it right.

When you are creating a new AI application, you are going to have to outline different tasks which require different testing tools. You are also going to have to test the application carefully. All types and methods of testing can be divided into several large groups: by goals, by the chronology of execution, by formality, by the degree of automation, by the purpose of the systems, by the volume of the tested software, by the level of testing, by the performers of testing and by the execution of the code. You will have a more feasible AI application if you follow this approach.

Why is testing AI software applications so important?

AI has helped improve software development. However, there is still a need to test the applications carefully.

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When you are developing a new AI application, you must admit that your work may contain errors. Then you must review your work to deal with these potential problems. It is better for someone outside to do it, because often mistakes are made due to incorrect assumptions or lack of knowledge in a certain area, and an outsider will be able to evaluate the application with a fresh look and from a different angle. You might want to try something like Fireart – Digital Product Agency, which is great for testing AI applications during the initial development cycle.

Testing AI software is a detailed process that involves executing a program with the intent to find bugs. Everyone makes mistakes, which is why the testing process is an important step in software development. Mistakes can be minor, and they can be dangerous or lead to significant financial losses for any company that relies on your artificial intelligence algorithms.

Modern methods of software development take a more systematic approach to testing, in accordance with the IBM Rational Unified Process (RUP). Testing is one of the RUP disciplines that AI software developers must practice. It focuses primarily on quality assessment using the following methods:

  • Search and documentation of quality defects in AI algorithms;
  • General recommendations regarding quality;
  • Checking the fulfillment of basic assumptions and requirements on specific examples;
  • Verifying that the AI application functions as designed;
  • Verification that the requirements are met accordingly.

The one who does nothing is not mistaken. The process of identifying software bugs is very important for the following reasons:

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  • Testing AI software applications is necessary to identify shortcomings and errors made during development;
  • Software testing is vital to improve the reliability and quality of software and to meet customer / customer expectations. A satisfied customer will come back to you again;
  • Stable software requires less maintenance, runs more accurately, consistently and delivers reliable results, which also leads to meeting customer / customer expectations.

It is very important to identify errors in the early stages of development, since later the elimination of such errors can entail significant costs or even require starting from scratch. However, bringing a software application with malfunctioning AI algorithms to market would be a much more serious and costlier problem.

Testing can significantly improve the performance of a software application, especially if it relies heavily on AI algorithms. Testing is essential to STAY in business as a software developer.

At an early stage of the development process, test design is used to help eliminate errors from the code. At this stage, you are working with the requirements and specifications of the project.

Static testing is an attempt to find errors without running the program. It is performed by viewing documentation (including source code) and static analysis. This is a useful and inexpensive form of testing.

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In dynamic testing, the program code is run / executed to detect errors. For example: unit testing, integration testing and system testing.

You have to plan what you want to do. A product design and development company monitors the testing activities, reports the progress of testing and the status of the software within the test.

To prepare, you need to choose the type of testing, test conditions and develop design tests.

During the evaluation, you must check the test results and evaluate the tested software, as well as conclude whether the software meets its specifications and whether the product has passed the tests.

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Along with testing, it is also necessary to create a reference section and a user manual.

Testing is Essential for Bringing High Quality AI Software Applications to Market

AI technology is becoming more sophisticated by the day, which provides a lot of value to companies that use it. This is due to the fact that software developers test their applications very carefully. If you want to create a successful software program, you can’t afford to be careless when you test it.

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It is best to begin testing with the regulations stage, accompanying the program software with tests at each subsequent step. The earlier testing starts in the product life cycle, the more you can be sure of its quality, which means that your users will be loyal and satisfied!

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Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.