Touchless testing platform

A computer-implemented method executed by one or more processors, the method comprising:

• receiving a code base and test results of execution runs of the code base;
• determining churn data for the code base based on an analysis of configuration and coding changes to the code base;
• processing the test results, the churn data, and the code base using an Artificial Intelligence (AI) model to determine a likelihood of failure for at least one of functionalities of the code base, the AI model trained using training data comprising resolutions to reported defects for the code base;
• generating a graphical representation based on the likelihood of failure for the at least one of the functionalities of the code base;
• processing the graphical representation using a machine-learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases;
• sequencing the set of test cases based on the priority values;
• and
• transmitting the sequenced set of test cases to a test execution engine.

One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

• receiving a code base and test results of execution runs of the code base;
• determining churn data for the code base based on an analysis of configuration and coding changes to the code base;
• processing test results, the churn data, and the code base using an Artificial Intelligence (AI) model to determine a likelihood of failure for at least one of functionalities of the code base, the AI model trained using training data comprising resolutions to reported defects for the code base;
• generating a graphical representation based on the likelihood of failure for the at least one of the 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 and to assign a priority value to each of the selected test cases;
• sequencing the set of test cases based on the priority values;
• and
• transmitting the sequenced set of test cases to a test execution engine.

A system, comprising:

• one or more processors;
• and
• a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a code base and test results of execution runs of the code base;
• determining churn data for the code base based on an analysis of configuration and coding changes to the code base;
• processing test results, the churn data, and the code base using an Artificial Intelligence (AI) model to determine a likelihood of failure for at least one of functionalities of the code base, the AI model trained using training data comprising resolutions to reported defects for the code base;
• generating a graphical representation based on the likelihood of failure for the at least one of the functionalities of the code base;
• processing the graphical representation using a machine-learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases;
• sequencing the set of test cases based on the priority values;
• and
• transmitting the sequenced set of test cases to a test execution engine.