Whether it is about organizing the process, performing repetitive mundane work, or providing reports on code quality, artificial intelligence is always one step ahead. A majority of companies have been shifting from manual software testing to automated algorithms lately. The use of AI in software testing can help organizations reach an incredible amount of optimization.
Let us take a look at 5-reasons on how AI can transform software testing and why you should adopt this technology:
1. API Test Generation Automation
API quality analysis allows you to measure the quality of interaction between multiple programs that communicate with servers, databases, and use different protocols. AI in software testing monitors the connected application’s functionality, analyzes potential areas for bugs and errors, and helps in creating test cases. AI can analyze large data volumes within seconds, and quickly pinpoint whether an API is performing well, and can generate accurate test cases.
2. Better Predictive Analysis
By incorporating artificial intelligence in software testing, you can use existing customer data and analytics to determine user’s browsing habits. It will enable developers and QA engineers to plan for the evolving user’s standards, thus offering better service quality. With machine learning, engineers can better analyze each user’s behavior, while AI platforms provide precise predictions.
3. Rectify Selenium Tests Automatically
Selenium tests are complex and take a lot of time to execute. Even a minor technical issue might result in loss of test case progress. Also, if the automated testing requirements are not properly defined, the test case will not be executed properly. AI in software testing easily identifies broken test cases and repairs them. In case the test execution suddenly stops because of a technical error, AI will analyze it and find a suitable solution.
4. UI Automation Testing
User interface testing is a part of quality assurance where engineers ensure that the UI for the application or software is delivered as expected. Automating UI testing is difficult as it contains multiple scenarios of bugs and errors. Artificial intelligence accurately analyzes the environment in which your application will run, for example, hardware requirements, browsers, and operating systems, and provides solutions regarding which UI standards fit your product. AI-based scenarios better adapt to user’s needs in comparison to automated UI tests.
5. Improve Quality of Test Cases
Instead of executing large test cases for detecting minor bugs, AI will run precise test cases that are easy to control and execute. When testing engineers run test cases, they do not have additional time to analyze other possible test cases. Due to this, they end up generating the least redundant data. AI, on the other hand, requires a minimal number of tests to analyze whether the changes made in the code have a positive or negative impact. There will be no redundant data, manual involvement, or bottlenecks, leading to enhanced test case quality for automated testing.
Artificial intelligence provides a lot of opportunities for automated software testing optimization. QASource offers best-in-the-industry quality assurance services by incorporating artificial intelligence in software testing. Contact us now to avail the benefits of AI and machine learning in software testing.