Robustness (computer science): Difference between revisions

Revision as of 06:45, 29 April 2020

Revision as of 20:16, 28 August 2020

Line 1:

{{see also|Fault-tolerant computer system}}

{{Complex systems}}

In [[computer science]], '''robustness''' is the ability of a computer system to cope with errors during execution<ref>{{cite web|url=http://dl.ifip.org/db/conf/pts/testcom2005/FernandezMP05.pdf |title=A Model-Based Approach for Robustness Testing |website=Dl.ifip.org |date= |accessdate=2016-11-13}}</ref><ref name="IEEE">1990. IEEE Standard Glossary of Software Engineering Terminology, IEEE Std 610.12-1990 defines robustness as "The degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions"</ref> and cope with erroneous input.<ref name="IEEE"/> Robustness can encompass many areas of computer science, such as [[Defensive programming|robust programming]], [[Overfitting|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. Various commercial products perform robustness testing of software analysis.<ref>{{cite journal|url=http://www.stanford.edu/~bakerjw/Publications/Baker%20et%20al%20(2008)%20Robustness,%20Structural%20Safety.pdf |title= On the assessment of robustness|accessdate=2016-11-13|doi=10.1016/j.strusafe.2006.11.004|volume=30|year=2008|journal=Structural Safety|pages=253–267 | last1 = Baker | first1 = Jack W. | last2 = Schubert | first2 = Matthias | last3 = Faber | first3 = Michael H.|issue= 3}}</ref>

== Introduction ==

Line 29:

===Robust machine learning===

Robust machine learning typically refers 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 is stable after adding some noise to the dataset.<ref>{{cite web|author=El Sayed Mahmoud |url=https://www.researchgate.net/post/What_is_the_definition_of_the_robustness_of_a_machine_learning_algorithm |title=What is the definition of the robustness of a machine learning algorithm? |publisher=ResearchGate |date= |accessdate=2016-11-13}}</ref>

===Robust network design===

Line 36:

=== Robust algorithms ===

There exists algorithms that tolerate errors in the input<ref>{{cite book |last1=Carbin |first1=Michael |title=Proceedings of the 19th international symposium on Software testing and analysis - ISSTA '10 |last2=Rinard |first2=Martin C. |chapter=Automatically identifying critical input regions and code in applications |date=12 July 2010 |pages=37–48 |doi=10.1145/1831708.1831713 |publisher=ACM|chapter-url=http://people.csail.mit.edu/rinard/paper/issta10.pdf |isbn=9781605588230 |s2cid=1147058 }}</ref> or during the computation.<ref name="Danglot">{{cite journal |last1=Danglot |first1=Benjamin |last2=Preux |first2=Philippe |last3=Baudry |first3=Benoit |last4=Monperrus |first4=Martin |title=Correctness attraction: a study of stability of software behavior under runtime perturbation |journal=Empirical Software Engineering |date=21 December 2017 |volume=23 |issue=4 |pages=2086–2119 |doi=10.1007/s10664-017-9571-8 |url=https://hal.archives-ouvertes.fr/hal-01378523/document|arxiv=1611.09187 |s2cid=12549038 }}</ref> In that case, the computation eventually converges to the correct output. This phenomenon has been called "correctness attraction".<ref name="Danglot"/>

==See also==