Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour Social network analysis Small-world networks Centrality Motifs Graph theory Scaling Robustness Systems biology Dynamic networks Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics Reaction–diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication Conversation theory Entropy Feedback Goal-oriented Homeostasis Information theory Operationalization Second-order cybernetics Self-reference System dynamics Systems science Systems thinking Sensemaking Variety Ordinary differential equations Phase space Attractors Population dynamics Chaos Multistability Bifurcation Rational choice theory Bounded rationality In computer science, robustness is the ability of a computer system to cope with errors during execution[1][2] and cope with erroneous input.
Generalizing test cases is an example of just one technique to deal with failure—specifically, failure due to invalid user input.
[4] Programs and software are tools focused on a very specific task, and thus are not generalized and flexible.
When applying the principle of redundancy to computer science, blindly adding code is not suggested.
Blindly adding code introduces more errors, makes the system more complex, and renders it harder to understand.
But as a system adds more logic, components, and increases in size, it becomes more complex.
One of the main reasons why there is no focus on robustness today is because it is hard to do in a general way.
[7] It requires code to handle these terminations and actions gracefully by displaying accurate and unambiguous error messages.
[8] Recently, consistently with their rise in popularity, there has been an increasing interest in the robustness of neural networks.