ofgogl.blogg.se - Sequential testing processing

#SEQUENTIAL TESTING PROCESSING FULL#
#SEQUENTIAL TESTING PROCESSING SOFTWARE#
#SEQUENTIAL TESTING PROCESSING CODE#
#SEQUENTIAL TESTING PROCESSING SERIES#

The modular implementation of the process model minimizes the amount of code changes you need to make when updating framework functionality. You can use the TestStand process models to create an extremely powerful and flexible test application. You can also create custom plug-ins to implement custom logging mechanisms The process model calls into plug-ins to perform result processing, including report generation and database logging. You can modify these plug-ins or create your own to extend the functionality of the process model without modifying the process models themselves. The process models call plug-in sequence files to implement result processing, such as report generation and database logging. The TestStand process models are further modularized through the plug-in architecture. Using a process model to perform common tasks allows for increased modularity and reusability because you can make modifications to common operations in only one location, while still keeping them separate from the underlying test executive.

If common operations are implemented directly in the test executive, making changes to the common test operations requires updates to the entire test executive.

If the test code is responsible for these common operations, each new test set created will need to repeat this code.

Typically, these applications do not have the concept of a process model, and either the test sequence or the test executive itself provides the mechanism for common testing tasks. Process models set TestStand apart from most home-grown test executives. Process models provide an additional layer, separate from both the test executive and test code, to implement common testing functionality This modularity allows you to customize the process models without affecting the test executive itself. In TestStand, the process model layer is implemented in sequence files, and is distinct from the TestStand engine. The set of such operations and their flow of execution is called a process model. Common operations that define the testing process include identifying the UUT, notifying the operator of pass/fail status, logging results, and generating a test report.

#SEQUENTIAL TESTING PROCESSING SERIES#

Usually, the test system must perform a series of operations before, during, and after it executes the sequence that performs the tests.

#SEQUENTIAL TESTING PROCESSING FULL#

The ASN can also be large if there is a mismatch between the data and the H 0 and H 1 models.Creating a full featured test for a product requires more than just executing a set of test cases. The test is the same, except a decision is made at a certain maximum sample size. A modified SPRT, called the Truncated Sequential Probability Ratio Test (TSPRT), addresses this issue. Unless an upper bound is specified, the ASN can become much larger than the amount of data available. These three conditions are represented as decision regions (accept/no decision/reject) in the above image.Ī and B are relatively simple to calculate with the following formulas:Ī major issue with the SPRT is that the optimality of the test only applies to simple hypotheses (e.g.

Reject the null hypothesis in favor of H 1: λ (n) ≥ A.

No conclusion ( resample): B ≤ λ (n) < A.

These define the conditions under which the null hypothesis will be rejected or not: However, you do need to specify some conditions, including two constants, A and B (where A > B).

#SEQUENTIAL TESTING PROCESSING SOFTWARE#

Likelihood ratio tests are extremely difficult to perform by hand, and so software is necessary. SPRT is based on the likelihood ratio statistic λ n. The ASN for the SPRT is lower than all other sequential tests and is usually lower than traditional, fixed-size sampling methods. The boundary of the decision region depends on the expected value of this random variable, called the Average Sample Number (ASN). With Wald’s SPRT, the amount of data points required to come to a conclusion can be defined by a random variable, called the sample number N s. Sequential analysis hypothesis testing generally enables a researcher to come to a conclusion with a minimum amount of data. You keep on repeating this process until you have a sound conclusion, so you don’t know the how big your sample will be until you’re finished testing.

If you fail to reach a conclusion, you repeat the sampling and then the hypothesis test.

Fail to reach either conclusion and continue sampling.

Reject the null hypothesis (H 0) in favor of the alternate hypothesis (H 1) and stop,.Sequential sampling works in a very non-traditional way instead of a fixed sample size, you choose one item (or a few) at a time, and then test your hypothesis.