Robustness

The term robustness (Latin robustus, from robur hard, oak ) refers to the ability of a system to withstand changes without adjusting his initially stable structure. It's often useful to specify, while the system is robust ( eg to change in ambient temperature or against faulty operation ).

Robustness in various disciplines

Computer science

In computer science and software development, the term " robustness " means the ability of a process to operate reliably even under adverse conditions. Robustness, also referred to as " fault tolerance ", is one of the quality criteria for software.

Examples of appropriate precautions are preventing undefined states and " crashes " (eg by full and detailed evaluation of the response code after execution of subroutines or system calls ), and in particular the interception of incorrect user or data entry (such as invalid command / function codes, incorrect formats in data fields, etc.).

As far as possible a "spectrum meaningful response options, depending on the situation " should be defined and applied, which can mean, depending on the expectations high Implemtierungsaufwand.

Nevertheless, a 100 percent robustness will be unavailable, such as when required system software components are missing or not working correctly. But even in such cases, a computer program may still produce a meaningful error message as possible and finish controlled himself.

Economy

In industry, the term " Robust Production Process " used. For the automotive industry, there is this one VDA volume in the series " The common quality management in the supply chain " with the title " product manufacturing and supply, robust production process." After that, a robust production process is characterized in that it is insensitive to unwanted influences and ensures on-time and on call friendly production with excellent quality in compliance with the planned economic costs. The definition of the " robust production process " is also the definition of " barriers " that describe the path of realization.

Analysis, diagnostics

In the analysis or diagnosis, the robustness of an analytical system allowed a certain variability of the sample to be analyzed (eg, sample pre-treatment is not needed) and / or other defined physical parameters during the measurement process and still provides reproducible and standardized results.

Statistics

In the inferential robustness means that a test even in injured conditions (eg normal distribution to small sample size ) works reliably and the error 1st and 2nd type changes only slightly. For lack of robustness condition caused by injury or increased type 1 error type 2 and either lead to progressive ( erroneous rejection of the null hypothesis ) or conservative decisions ( falsely retaining the null hypothesis ).

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