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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.
Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location , scale , and regression parameters .
Robustness testing is any quality assurance methodology focused on testing the robustness of software.Robustness testing has also been used to describe the process of verifying the robustness (i.e. correctness) of test cases in a test process.
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 ...
Robust measures of scale can be used as estimators of properties of the population, either for parameter estimation or as estimators of their own expected value.. For example, robust estimators of scale are used to estimate the population standard deviation, generally by multiplying by a scale factor to make it an unbiased consistent estimator; see scale parameter: estimation.
Mutational robustness (also called mutation tolerance) describes the extent to which an organism's phenotype remains constant in spite of mutation. [9] Robustness can be empirically measured for several genomes [10] [11] and individual genes [12] by inducing mutations and measuring what proportion of mutants retain the same phenotype, function or fitness.
In his book, Taleb stresses the differences between antifragile and robust/resilient. "Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better." [1] The concept has now been applied to ecosystems in a rigorous way. [17]
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 ...