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It has always been an important development strategy for software to keep pace with its evolution and meet stakeholder needs. Therefore, many software systems have very short release cycles. When many releases appear, how to test them becomes more and more important. Since the new features are often innovative, there are no conventional tools to test them, and manual testing is actually very cumbersome, not to mention that there are many non-testable features. This thesis mainly investigates the testability of the 109 features of 16 versions of the Mozilla Firefox browser from May, 2022 to July, 2023. The objects are the features of Firefox browser itself under the Windows 11 operating system, excluding additional plug-ins or Firefox browsers under other operating systems. Through manual testing and analyses, the features of the Firefox browser are divided into testable and non-testable, and the reasons why features are non-testable are given. Since the object being tested is the features of the browser, Selenium—a tool dedicated to testing browsers is considered. In this thesis, the Selenium package based on Python is used to simulate manual testing. By comparing the results of manual testing and the results generated by Selenium, we can gain insights into the limitations of Selenium testing, which contributes to improving automated testing in the future work. In this thesis, we also employ OpenAI’s ChatGPT (version 3.5) to assess the testability of each feature. We then compare ChatGPT’s answers with the manual analysis results.