Touchless testing platform

1. 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;

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;

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.

2. The method of claim 1 , wherein the graphical representation is a heat map.

3. The method of claim 1 , wherein processing the log file through the pattern-mining algorithm further determines a failure pattern, a business flow graph or an anomaly.

4. The method of claim 3 , further comprising:

generating a report based on the usage pattern, the failure pattern, the business flow graph, or the anomaly; and

transmitting the report to a control center application.

5. The method of claim 1 , further comprising:

analyzing the usage pattern to identify pattern groups; and

generating metadata for the identified pattern groups.

6. The method of claim 1 , further comprising,

determining churn data based on an analysis of configuration and coding changes to the code base;

processing test results, the churn data, and the code base through an Artificial Intelligence (AI) model, the AI model trained using training data comprising resolutions to reported defects for the code base; and

determining a likelihood of failure for at least one of functionalities of the code base, where in the graphical representation is generated further based on the likelihood of failure for the at least one of the functionalities of the code base.

7. The method of claim 6 , wherein the graphical representation depicts testing priorities of the functionalities of the code base.

8. The method of claim 1 , wherein the test cases are selected and assigned a priority based on a criterion.

9. The method of claim 8 , wherein the criterion is a maximization of defect yield per test case executed.

10. 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 log file that includes log records generated from a code base;

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;

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.

11. The one or more non-transitory computer-readable of claim 10 , wherein the operations further comprise:

demining a failure pattern based on an analysis of testing results prior testing to the code base, wherein the each of the test cases in the set of test cases is further selected based on the failure pattern, and wherein the assigned priority value for each of the test cases in the set of test cases is further determined based on the failure pattern.

12. The one or more non-transitory computer-readable of claim 10 , wherein the machine-learning algorithm is a reward-seeking algorithm trained to maximize defect yield per test case.

13. The one or more non-transitory computer-readable of claim 10 , wherein the operations further comprise:

before receiving the log file, extracting terminologies from requirement documents;

classifying the extracted terminologies into categories based on a corpus of known terms; and

generating process maps based on the classified terminologies.

14. The one or more non-transitory computer-readable of claim 10 , wherein the operations further comprise:

before selecting a set of test cases, clustering the plurality of test cases for the code base based on contextual similarity.

15. 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 log file that includes log records generated from a code base;

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;

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.

16. The system of claim 15 , wherein the operations further comprise:

before receiving the log file, receiving a test scenario and a context file selected through a user interface, the context file including an object map comprising objects that correlate to respective components of a display page for the code base, the test scenario describing one of the test cases involving an intended interaction with at least one of the components on the display page;

correlating the intended interaction with the at least one component with the corresponding object in the object map;

processing the intended interaction and the corresponding object through an Artificial Intelligence (AI) model, the AI model trained using training data comprising a plurality of processes and respective process steps supported by the components of the display page;

determining a script template based on the processing and a selected automation tool;

applying, based on the processing, the script template to the intended interaction and the correlated object to generate an automated testing script for the selected automating tool; and

assigning the generated automated testing script to the one of the test cases.

17. The system of claim 16 , wherein each of the test scenarios is represented by a process graph that depicts a hierarchy of business process.

18. The system of claim 15 , wherein the operations further comprise:

before receiving the log file, determining a contextual distance for each of the test cases respective to each other;

assigning each of the test cases to at least one cluster based on the contextual distances; and

assigning a score value to each test cases based on a robustness to detect defects respective to the other test cases in the at least one cluster.

19. The system of claim 18 , wherein the contextual distance for each test case includes a semantic distance for each of the test cases respective to each other, a defect distance for each of the test cases respective to each other, and a code hit distance for each of the test cases respective to each other.

20. The system of claim 15 , wherein the operations further comprise:

determining a resolution for a defect, the defect reported based on a result of an execution a testing script assigned to one of the sequence set of test cases; and

implementing the resolution in the code base.