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An Integrated Quantitative Approach to Acceptance Testing and Related Decisions
Author(s) -
MacCarthy John
Publication year - 2019
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2019.00661.x
Subject(s) - acceptance testing , test (biology) , computer science , test strategy , decision tree , reliability engineering , margin (machine learning) , scope (computer science) , confidence interval , statistics , data mining , machine learning , engineering , mathematics , software engineering , paleontology , software , biology , programming language
This paper provides an integrated, analytical framework (and associated tools) for determining the scope of an acceptance test, designing acceptance tests, and for making acceptance decisions. A decision tree model is developed that may be used to determine the value of different acceptance test designs. Hypothesis testing models are developed that may be used to determine the performance threshold for testing, given a desired confidence of failing a bad system (test “Power”), and the design margin required to have a given confidence of passing a good system (test “Confidence”). These two models are then integrated into a single model that may be used to establish a test design (in terms of an acceptance threshold and the number of measurements) that achieves a desired test Power and Confidence. Finally a decision tree model is developed for the system acceptance decision based on the results of a given acceptance test.