Robustness (computer science)

In computer science, robustness is a ability of the computer system to cope with errors during execution and cope with erroneous input. Robustness can encompass many areas of computer science, such(a) as robust programming, robust machine learning, as well as 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. Various commercial products perform robustness testing of software analysis.

Areas

Robust programming is a line of programming that focuses on handling unexpected termination together with unexpected actions. It requires script to handle these terminations and actions gracefully by displaying accurate and unambiguous error messages. These error messages permit the user to more easily diagnose the program.

Robust machine learning typically intended to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance isafter adding some noise to the dataset.

Robust network an arrangement of parts or elements in a particular form figure or combination. is the discussing of network cut in the face of variable or uncertain demands. In a sense, robustness in network design is broad just like robustness in software design because of the vast possibilities of refine or inputs.

There exists algorithms that tolerate errors in the input or during the computation. In that case, the computation eventually converges to the adjustment output. This phenomenon has been called "correctness attraction".