With today’s focus on Continuous Integration, test cases are used to ensure the software’s reliability when integrating and developing code. Test cases that behave in an undeterministic manner are known as flaky tests, which threatens the software’s reliability. Because of flaky test’s undeterministic nature, they can be troublesome to detect and correct. This is causing companies to spend great amount of resources on flaky tests since they can reduce the quality of their products and services. The aim of this thesis was to develop a usable tool that can automatically detect flakiness in the Randomness category. This was done by initially locating and rerunning flaky tests found in public Git repositories. By scanning the resulting pytest logs from the tests that manifested flaky behaviour, noting indicators of how flakiness manifests in the Randomness category. From these findings we determined tracing to be a viable option of detecting Randomness as a cause of flakiness. The findings were implemented into our proposed tool FlakyReporter, which reruns flaky tests to determine if they pertain to the Randomness category. Our FlakyReporter tool was found to accurately categorise flaky tests into the Randomness category when tested against 25 different flaky tests. This indicates the viability of utilizing tracing as a method of categorizing flakiness.