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Fault injection is a testing method that can be used for checking the robustness of systems. During the process, testing engineers inject faults into systems and observe the system's resiliency. [4] Test engineers can develop efficient methods which aid fault injection to find critical faults in the system. [5] [6]
Robust statistical methods, of which the trimmed mean is a simple example, seek to outperform classical statistical methods in the presence of outliers, or, more generally, when underlying parametric assumptions are not quite correct.
Although uptake of robust methods has been slow, modern mainstream statistics text books often include discussion of these methods (for example, the books by Seber and Lee, and by Faraway [vague]; for a good general description of how the various robust regression methods developed from one another see Andersen's book [vague]).
Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness ...
Robustification as it is defined here is sometimes referred to as parameter design or robust parameter design (RPD) and is often associated with Taguchi methods.Within that context, robustification can include the process of finding the inputs that contribute most to the random variability in the output and controlling them, or tolerance design.
Robustness is the property of being strong and healthy in constitution. When it is transposed into a system, it refers to the ability of tolerating perturbations that might affect the system's functional body. In the same line robustness can be defined as "the ability of a system to resist change without adapting its initial stable ...
In robust statistics, more importance is placed on robustness and applicability to a wide variety of distributions, rather than efficiency on a single distribution. M-estimators are a general class of estimators motivated by these concerns. They can be designed to yield both robustness and high relative efficiency, though possibly lower ...
Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness, [1] prompting research to improve them. This was the start of the theory of ...