END-TO-END COMPUTER SYSEM TESTING
A software process is tested using an instance of a computer system. A first set of data and a second set of data are collected. The first set relates to resources used by the software process during the testing and the second set relates to conditions of the instance during the testing. A machine learning algorithm is trained with the first set of data and the second set of data, thereby generating a model relating to the resources used by the software process under the conditions of the instance. User input is received, the user input relates to expectations of a test plan for the software test. The test plan is created as a function of the model and the user input.
1 . A process for executing an end-to-end computer system test comprising:
testing a software process using an instance of a computer system;
collecting a first set of data relating to resources used by the software process during the testing;
collecting a second set of data relating to conditions of the instance during the testing;
training a machine learning algorithm with the first set of data and the second set of data, thereby generating a model relating to the resources used by the software process under the conditions of the instance;
receiving user input relating to one or more expectations of a test plan for the end-to-end computer system test; and
creating the test plan as a function of the model and the user input.
2 . The process of claim 1 , wherein the testing the software process, the collecting the first set of data, the collecting the second set of data, and the training the machine learning algorithm are executed for a plurality of software processes in the computer system.
3 . The process of claim 1 , wherein the testing the software process, the collecting the first set of data, the collecting the second set of data, and the training the machine learning algorithm are executed for a plurality of software processes in the computer system and are executed for a plurality of computer systems over a time period, such that the machine learning algorithm continuously learns over the time period.
4 . The process of claim 1 , wherein the end-to-end computer system test occurs in a cloud-based computer environment.
5 . The process of claim 1 , wherein the end-to-end computer system test occurs in a non-cloud-based environment.
6 . The process of claim 1 , wherein the instance is associated with one or more resources of the computer system.
7 . The process of claim 6 , wherein the one or more resources of the computer system comprise one of more of a central processing unit (CPU), a memory, an interface, and a bus.
8 . The process of claim 1 , wherein the conditions relate to one or more other software processes executing on the computer system, a time of day of the testing, and a load on the computer system.
9 . The process of claim 1 , wherein the expectations comprise one or more of a cost of the testing, a speed of the testing, a duration of the testing, and a relationship between one or more tests.
10 . The process of claim 1 , wherein the test plan comprises one or more of a recommendation of how many instances to use and a recommendation of stacking of a plurality of software processes.
11 . A non-transitory machine-readable medium comprising instructions that when executed by a processor executes a process comprising:
testing a software process using an instance of a computer system;
collecting a first set of data relating to resources used by the software process during the testing;
collecting a second set of data relating to conditions of the instance during the testing;
training a machine learning algorithm with the first set of data and the second set of data, thereby generating a model relating to the resources used by the software process under the conditions of the instance;
receiving user input relating to one or more expectations of a test plan for the end-to-end computer system test; and
creating the test plan as a function of the model and the user input.
12 . The non-transitory machine-readable medium of claim 11 , wherein the testing the software process, the collecting the first set of data, the collecting the second set of data, and the training the machine learning algorithm are executed for a plurality of software processes in the computer system.
13 . The non-transitory machine-readable medium of claim 11 , wherein the testing the software process, the collecting the first set of data, the collecting the second set of data, and the training the machine learning algorithm are executed for a plurality of software processes in the computer system and are executed for a plurality of computer systems over a time period, such that the machine learning algorithm continuously learns over the time period.
14 . The non-transitory machine-readable medium of claim 1 , wherein the end-to-end computer system test occurs in a cloud-based computer environment.
15 . The non-transitory machine-readable medium of claim 11 , wherein the end-to-end computer system test occurs in a non-cloud-based environment.
16 . The non-transitory machine-readable medium of claim 11 , wherein the instance is associated with one or more resources of the computer system; and wherein the one or more resources of the computer system comprise one of more of a central processing unit (CPU), a memory, an interface, and a bus.
17 . The non-transitory machine-readable medium of claim 11 , wherein the conditions relate to one or more other software processes executing on the computer system, a time of day of the testing, and a load on the computer system.
18 . The non-transitory machine-readable medium of claim 11 , wherein the expectations comprise one or more of a cost of the testing, a speed of the testing, a duration of the testing, and a relationship between one or more tests.
19 . The non-transitory machine-readable medium of claim 11 , wherein the test plan comprises one or more of a recommendation of how many instances to use and a recommendation of stacking of a plurality of software processes.
20 . A system comprising:
a computer processor; and
a memory coupled to the computer processor;
wherein one or more of the computer processor and memory are operable for:
testing a software process using an instance of a computer system;
collecting a first set of data relating to resources used by the software process during the testing;
collecting a second set of data relating to conditions of the instance during the testing;
training a machine learning algorithm with the first set of data and the second set of data, thereby generating a model relating to the resources used by the software process under the conditions of the instance;
receiving user input relating to one or more expectations of a test plan for the end-to-end computer system test; and
creating the test plan as a function of the model and the user input.