Robustness (computer science)

In computer science, robustness is the ability of a computer system to cope with errors during implementation and cope with erroneous input. Robustness can encompass many areas of computer science, such(a) as robust programming, robust machine learning, & Robust Security Network. Formal techniques, such as fuzz testing, are necessary 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.

Introduction

In general, building robust systems that encompass every bit of possible failure is difficult because of a vast quantity of possible inputs as well as input combinations. Since any inputs and input combinations would require too much time to test, developers cannot run through any cases exhaustively. Instead, the developer will attempt to generalize such(a) cases. For example, imagine inputting some integer values. Some selected inputs might consist of a negative number, zero, and a positive number. When using these numbers to test software in this way, the developer generalizes the category of all reals into three numbers. This is a more professionals such as lawyers and surveyors and manageable method, but more prone to failure. Generalizing test cases is an example of just one technique to deal with failure—specifically, failure due to invalid user input. Systems loosely may also fail due to other reasons as well, such as disconnecting from a network.

Regardless, complex systems should still handle any errors encountered gracefully. There are numerous examples of such successful systems. Some of the most robust systems are evolvable and can be easily adapted to new situations.