Selecting test-templates using template-aware coverage data
View Patent ↗An example system includes a processor to receive a template-aware coverage data that tracks probabilities of events in a list of events being hit for a set of test-templates over a first and second predetermined period of time. The processor is to generate a hit prediction score for each combination of unhit event in the events and each test-template in the set of test-templates of the second predetermined period of time. The hit prediction score indicates a probability of an unhit event being hit by a particular test-template in a future third predetermined period of time based on the template-aware coverage data and similarities between the events and the test-templates. The processor is to generate a template score for each test-template based on the hit prediction scores for each test-template. The processor is to select a test-template from the set of test-templates based on the template score.
1. A system, comprising a processor to:
receive template-aware coverage data that tracks probabilities of events in a list of events being hit for a set of test-templates over a first and second predetermined period of time;
generate a hit prediction score for each combination of unhit events in the list of events and test-template in the set of test-templates of the second predetermined period of time, wherein the hit prediction score indicates a probability of an unhit event being hit by a particular test-template in a future third predetermined period of time based on the template-aware coverage data and similarities between the events and the test-template, and wherein the hit prediction score is generated by a classifier, the classifier trained using extracted features from template-aware coverage data of the first predetermined period of time and labels extracted from template-aware coverage data of the second predetermined time period of times;
generate a template score for each test-template based on the hit prediction scores for each test-template; and
select a test-template from the set of test-templates based on the template score.
2. The system of claim 1 , wherein the template score of each test-template comprises an average of hit prediction scores associated with each test-template.
3. The system of claim 1 , wherein the template-aware coverage data comprises a template-aware coverage matrix.
4. The system of claim 1 , wherein the processor is to generate a policy comprising the selected test-template and transmit the policy to a tester.
5. The system of claim 1 , wherein the processor is to execute the selected test-template to hit an unhit event.
6. The system of claim 1 , wherein the processor is to generate a policy comprising unhittable events and transmit the policy to a tester.
7. A computer-implemented method, comprising:
receiving, via a processor, a template-aware coverage data that tracks probabilities of events in a list of events being hit for a set of test-templates over first and second predetermined period of time;
generating, via the processor, a hit prediction score for each combination of unhit event in the list of events and test-template in the set of test-templates of the second predetermined period of time, wherein the hit prediction score indicates a probability of an unhit event being hit by a particular test-template in a future third predetermined period of time based on the template-aware coverage data and similarities between the events and the test-templates, wherein generating the hit prediction score comprises inputting the template-aware coverage data into a trained classifier, the trained classifier trained to generate hit prediction scores using extracted features from template-aware coverage data of a first predetermined period of time and labels extracted from template-aware coverage data of the second predetermined period of time;
generating, via the processor, a template score for each test-template based on the hit prediction scores for each test-template; and
selecting, via the processor, a test-template from the set of test-templates based on the template score.
8. The computer-implemented method of claim 7 , comprising executing, via the processor, the test-template to hit an unhit event during testing, wherein an amount of a resource provided to the test-template during testing is based on the template score associated with the test-template.
9. The computer-implemented method of claim 7 , comprising generating, via the processor, a policy comprising the test-template and transmitting the policy to a tester, wherein the test-template is included in the policy in response to detecting that the template score of the test-template exceeds a threshold score.
10. The computer-implemented method of claim 7 , wherein generating the hit prediction score comprises extracting features from the template-aware coverage data of the first predetermined period of time, hashing the extracted features, and inputting the hashed extracted features into a trained classifier.
11. The computer-implemented method of claim 7 , wherein generating the hit prediction score comprises extracting features from the template-aware coverage data of the first predetermined period of time based on names of the events and the test-templates.
12. The computer-implemented method of claim 7 , wherein generating the template score for each test-template comprises averaging hit prediction scores associated with each test-template.
13. A computer program product for selecting test-templates, the computer program product comprising a computer-readable storage medium having program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program code executable by a processor to cause the processor to:
receive a template-aware coverage data that tracks probabilities of events in a list of events being hit for a set of test-templates over a first predetermined period of time and a second predetermined period of time;
input the template-aware coverage data into a trained classifier, the trained classifier trained to generate hit prediction scores using extracted features from template-aware coverage data of the first predetermined period of time and labels extracted from template-aware coverage data of the second predetermined period of time;
generate, via the trained classifier, a hit prediction score for each combination of unhit event in the list of events and test-template in the set of test-templates of the second predetermined period of time, wherein the hit prediction score indicates a probability of an unhit event being hit by a particular test-template in a future third predetermined period of time based on the template-aware coverage data and similarities between the events and the test-templates;
generate a template score for each test-template based on the hit prediction scores for each test-template; and
select a test-template from the set of test-templates based on the template score.
14. The computer program product of claim 13 , further comprising program code executable by the processor to execute the selected test-template to hit an unhit event.
15. The computer program product of claim 13 , further comprising program code executable by the processor to extract features from the template-aware coverage data of the first predetermined period of time, hash the extracted features, and input the hashed extracted features into a trained classifier.
16. The computer program product of claim 13 , further comprising program code executable by the processor to extract features from the template-aware coverage data of the first predetermined period of time based on names of the events and the test-templates.
17. The computer program product of claim 13 , further comprising program code executable by the processor to generate the template score for each test-template by averaging hit prediction scores associated with each test-template.