Test prioritization and dynamic test case sequencing

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

receiving a log file and testing results generated from a code base for an application;

processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base;

clustering defects from the testing results based on a respective functionality of the application reported within each of the defects;

generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base;

sequencing a set of the test cases based on the test prioritizations; and

transmitting the sequenced set of test cases to a test execution engine.

2. The method of claim 1 , wherein the testing prioritizations include a prioritization for each test case, wherein the weightages are assigned to each test case according to the usage pattern of the respective functionality or code module tested and the associated cluster of defects.

3. The method of claim 1 , further comprising:

before generating the testing prioritization, determining lines of code that are exercised by each of the test cases, wherein the weightages to the test cases are assigned based on the respective lines of code that are exercised.

4. The method of claim 3 , wherein the lines of code exercised by each test case are determined based on execution data from an execution of the test cases against the application captured by an agent installed on an application server that is running the application.

5. The method of claim 1 , further comprising:

before generating the testing prioritization, determining user behaviors on the application based on an analysis of the log file, wherein the weightages are assigned based on the respective test case mimicking the determined user behaviors.

6. The method of claim 1 , further comprising:

before generating the testing prioritization, processing the testing results to determine an effectiveness metric for each test case, wherein the effectiveness metric indicates an effectiveness of a test case for defect discovery, wherein the weightages to the test cases are assigned based on the effectiveness metric.

7. The method of claim 1 , wherein the defects are clustered based on semantics generated using text analytics or context generated using knowledge mapping.

8. The method of claim 1 , further comprising:

before generating the testing prioritization, clustering the test cases based on a functionality tested and at least one of the code modules that is exercised by the test case; and

mapping the clusters of defects to the to the clusters of test cases to determine test coverage for the code base, wherein the weightages to the test cases are assigned based on the mapping and according to the test coverage.

9. The method of claim 8 , further comprising:

identifying gaps in test coverage based on the mapping and the determined test coverage.

10. The method of claim 1 , the usage pattern includes sequences that code modules were executed and a frequency that each of the sequences were executed.

11. The method of claim 10 , further comprising:

generating, based on the usage pattern, a representation of code module execution, a distribution of data across service types, or test case utilization based on actual usages in production.

12. The method of claim 1 , further comprising,

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

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

determining a likelihood of failure for functionalities of the code base, wherein the weightages to the test cases are based on the likelihood of failure for the functionalities of the code base.

13. The method of claim 1 , further comprising:

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

implementing the resolution in the code base.

14. 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 and testing results generated from a code base for an application;

processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base;

clustering defects from the testing results based on a respective functionality of the application reported within each of the defects;

generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base;

sequencing a set of the test cases based on the test prioritizations; and

transmitting the sequenced set of test cases to a test execution engine.

15. The one or more non-transitory computer-readable storage media of claim 14 , wherein the testing prioritizations include a prioritization for each test case, wherein the weightages are assigned to each test case according to the usage pattern of the respective functionality or code module tested and the associated cluster of defects.

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

before generating the testing prioritization, determining lines of code that are exercised by each of the test cases, wherein the weightages to the test cases are assigned based on the respective lines of code that are exercised, and wherein the lines of code exercised by each test case are determined based on execution data from an execution of the test cases against the application captured by an agent installed on an application server that is running the application.

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

before generating the testing prioritization, determining user behaviors on the application based on an analysis of the log file, wherein the weightages are assigned based on the respective test case mimicking the determined user behaviors.

18. 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 and testing results generated from a code base for an application;

processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base;

clustering defects from the testing results based on a respective functionality of the application reported within the defect;

generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules;

sequencing a set of the test cases based on the test prioritizations; and

transmitting the sequenced set of test cases to a test execution engine.

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

before generating the testing prioritization, processing the testing results to determine an effectiveness metric for each test case, wherein the effectiveness metric indicates an effectiveness of a test case for defect discovery, and wherein the weightages to the test cases are assigned based on the effectiveness metric.

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

before generating the testing prioritization, clustering the test cases based on a functionality tested and at least one of the code modules that is exercised by the test case;

mapping the clusters of defects to the to the clusters of test cases to determine test coverage for the code base, wherein the weightages to the test cases are assigned based on the mapping and according to the test coverage; and

identifying gaps in test coverage based on the mapping and the determined test coverage.