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IEEE 1522
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IEEE 1522 2004 Edition, September 23, 2004 Trial-Use Standard for Testability and Diagnosability Characteristics and Metrics
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Description / Abstract:
Foreword
As systems became more complex, costly, and difficult to diagnose and repair, initiatives were started to address these problems. The objective of one initiative, testability, was to make systems easier to test. To be useful, this commercial standard must provide specific, unambiguous definitions of criteria for assessing system testability.
Recent initiatives by the Institute of Electrical and Electronics Engineers (IEEE), Inc. on standardizing test architectures have provided an opportunity to standardize testability metrics. The IEEE 1232TM Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) series of standards provide the foundation for precise and unambiguous testability and diagnosability metrics.
It is the objective of this standard to provide notionally correct and inherently useful but mathematically precise definitions of testability measures that may be used to either measure the testability characteristics of a system or to predict the testability of a system. Notionally correct means that the measures are not in conflict with intuitive and historical representations.
Scope
This standard defines technology-independent testability and diagnosability characteristics and metrics, particularly those based on relevant standard information models, including standard AI-ESTATE information models.
As systems became more complex, costly, and difficult to diagnose and repair, initiatives were started to address these problems. The objective of one initiative, testability, was to make systems easier to test. To be useful, this commercial standard must provide specific, unambiguous definitions of criteria for assessing system testability.
Recent initiatives by the Institute of Electrical and Electronics Engineers (IEEE), Inc. on standardizing test architectures have provided an opportunity to standardize testability metrics. The IEEE 1232TM Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) series of standards provide the foundation for precise and unambiguous testability and diagnosability metrics.
It is the objective of this standard to provide notionally correct and inherently useful but mathematically precise definitions of testability measures that may be used to either measure the testability characteristics of a system or to predict the testability of a system. Notionally correct means that the measures are not in conflict with intuitive and historical representations.
Scope
This standard defines technology-independent testability and diagnosability characteristics and metrics, particularly those based on relevant standard information models, including standard AI-ESTATE information models.