Log-based automation testing
A testing engine and/or other machine learning and artificial intelligence evaluates one or more user activity logs associated with one or more users and/or one or more software applications or apps. The testing engine generates one or more testing scripts based on one or more sequences of activities in the one or more user activity logs. Such testing scripts may then be used to perform one or more tests, such as using a testing automation framework and/or engine. In various examples, evaluating the one or more user activity logs and/or generating one or more testing scripts may involve categorizing one or more users, activities, and/or user activity logs; consolidating one or more users, activities, and/or user activity logs; modifying one or more user activity logs; and so on.
1. A method, comprising:
receiving multiple user activity logs;
identifying one or more statistically significant categories associated with one or more of the multiple user activity logs; and
generating one or more testing scripts based on the one or more statistically significant categories, the generating including generating the one or more testing scripts based on the one or more of the multiple user activity logs associated with the statistically significant categories.
2. The method of claim 1 , wherein the multiple user activity logs are generated by an application based on interaction with users.
3. The method of claim 1 , further comprising performing a test based on the one or more testing scripts.
4. The method of claim 3 , further comprising consolidating the user activity logs and performing the test based on the consolidated user activity logs.
5. The method of claim 3 , wherein the test is performed by an automation engine.
6. The method of claim 1 , further comprising anonymizing at least a portion of the multiple user activity logs.
7. The method of claim 1 , further comprising generating a stress testing script by changing a level of activity represented in the user activity logs.
8. The method of claim 1 , further comprising prompting a user to provide names, labels, or identifiers for the statistically significant categories.
9. The method of claim 1 , wherein identifying the statistically significant categories includes identifying categories associated with one or more user activity logs that make up more than a threshold percentage of total user activity logs.
10. A non-transitory computer storage medium having stored therein programming instructions that, when executed by one or more processors of an electronic device, cause the electronic device to perform operations comprising:
receiving multiple user activity logs;
identifying one or more statistically significant categories associated with one or more of the multiple user activity logs; and
generating one or more testing scripts based on the one or more statistically significant categories, the generating including generating the one or more testing scripts based on the one or more of the multiple user activity logs associated with the statistically significant categories.
11. The non-transitory computer storage medium of claim 10 , wherein the operations further comprise performing a test based on the one or more testing scripts.
12. The non-transitory computer storage medium of claim 11 , wherein the operations further comprise consolidating the user activity logs and performing the test based on the consolidated user activity logs.
13. The non-transitory computer storage medium of claim 11 , wherein the test is performed by an automation engine.
14. The non-transitory computer storage medium of claim 10 , wherein the operations further comprise generating a stress testing script by changing a level of activity represented in the user activity logs.
15. The non-transitory computer storage medium of claim 10 , wherein the operations further comprise prompting a user to provide names, labels, or identifiers for the statistically significant categories.
16. The non-transitory computer storage medium of claim 10 , wherein identifying the statistically significant categories includes identifying categories associated with one or more user activity logs that make up more than a threshold percentage of total user activity logs.
17. A system comprising:
one or more processors; and
programming instructions that, when executed by the one or more processors cause the system to perform operations including:
receiving multiple user activity logs;
identifying one or more statistically significant categories associated with one or more of the multiple user activity logs; and
generating one or more testing scripts based on the one or more statistically significant categories, the generating including generating the one or more testing scripts based on the one or more of the multiple user activity logs associated with the statistically significant categories.
18. The system of claim 17 , wherein the operations further include generating a stress testing script by changing a level of activity represented in the user activity logs.
19. The system of claim 17 , wherein the operations further include prompting a user to provide names, labels, or identifiers for the statistically significant categories.
20. The system of claim 17 , wherein identifying the statistically significant categories includes identifying categories associated with one or more user activity logs that make up more than a threshold percentage of total user activity logs.