The abbreviation stands for "load testing

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Studying the system. Before starting to write scenarios, testers collect all possible information about the system: what components it consists of, what terms describe it, and so on.

Defining the testing objectives. The specialists formulate why they will conduct the tests and what question they are going to answer with their help. For example, the goal may be to determine the maximum workload or to find “weak spots” in the system.

Development of MNT. Testing is carried out according to MNT. methodology" - it is different for each case. It is developed after collecting information and analyzing the goals. For example, when drawing up a methodology, they describe:

testing restrictions - what is not going to be tested, for russia telegram data example, components that have already been tested previously;
modeling rules - how and in what ways will increased load be simulated, for example using emulators;
possible errors - what exactly could hypothetically go wrong in the system and why;
test stand and tools - what are the characteristics of the test environment and the product itself, as well as the technologies that will be used in the tests;
required characteristics - what the product should be like according to the requirements;
testing stages - how exactly the load modeling will be carried out, what stages the tests will consist of;
list of results - what information can be obtained using tests and what methods will be used to analyze it later;
reporting requirements - what it will look like and what it will contain, for example, recommendations for improving performance.
Conducting tests. Based on the developed methodology, test cases are created, the environment is set up and testing is launched - the specified load is simulated, the results are looked at and described. As a rule, special software is used for this, which is capable of creating tests, generating requests to the application, and so on.

Collection and analysis of results. During tests, specialists collect results — information about the system's behavior in different test cases, response time, resource usage data, and others. Sometimes the collection and analysis of this data is also automated, but to draw conclusions from it, human participation is usually required.