A computer-implemented method executed by one or more processors, the method comprising: receiving a log file that includes log records generated from a code base; determining churn data based on an analysis of configuration and coding changes to the code base; processing test results of prior testing to the code base, the churn data, and the code base through an Artificial Intelligence model, the AI model trained using training data comprising resolutions to reported defects for the code base from the prior testing to the code base, the training data comprising churn data for the prior testing; determining a likelihood of failure for at least one of a plurality of functionalities of the code base based on processing the test results, the churn data, and the code base through the AI model wherein determining the likelihood of failure is based at least in part on failure patterns extracted from the test results, code quality, and churn affecting code quality for the functionalities, processing the log file through a pattern-mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern and the likelihood of failure for the at least one of the plurality of functionalities of the code base; processing the graphical representation through a machine-learning algorithm to select a set of test cases from a plurality of test cases for the code base, to assign a priority value to each of the selected test cases and to set an execution sequence of the set of test cases based on the priority values, wherein the machine-learning algorithm is a reward-seeking algorithm trained to maximize defect yield per test case; and transmitting the sequenced set of test cases to a test execution engine. receiving a log file that includes log records generated from a code base; determining churn data based on an analysis of configuration and coding changes to the code base; processing test results of prior testing to the code base, the churn data, and the code base through an Artificial Intelligence model, the AI model trained using training data comprising resolutions to reported defects for the code base from the prior testing to the code base, the training data comprising churn data for the prior testing; determining a likelihood of failure for at least one of a plurality of functionalities of the code base based on processing the test results, the churn data, and the code base through the AI model wherein determining the likelihood of failure is based at least in part on failure patterns extracted from the test results, code quality, and churn affecting code quality for the functionalities, processing the log file through a pattern-mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern and the likelihood of failure for the at least one of the plurality of functionalities of the code base; processing the graphical representation through a machine-learning algorithm to select a set of test cases from a plurality of test cases for the code base, to assign a priority value to each of the selected test cases and to set an execution sequence of the set of test cases based on the priority values, wherein the machine-learning algorithm is a reward-seeking algorithm trained to maximize defect yield per test case; and transmitting the sequenced set of test cases to a test execution engine.