principles:murphy_s_law
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It is not always possible to design a system in such a way. But as systems are built and used by humans, one should strive for such " | It is not always possible to design a system in such a way. But as systems are built and used by humans, one should strive for such " | ||
- | There are different kinds of possible errors that can and according to ML eventually will occur in some way: Replicated data can get out of sync, invariants can be broken, preconditions can be violated, interfaces can be misunderstood, | + | Note that Murphy' |
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- | Note that Murphy' | + | |
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* [[Uniformity Principle]] (UP): A typical source of mistakes are differences. If similar things work similarly, they are more understandable. But if there are subtle differences in how things work, it is likely that someone will make the mistake to mix this up. | * [[Uniformity Principle]] (UP): A typical source of mistakes are differences. If similar things work similarly, they are more understandable. But if there are subtle differences in how things work, it is likely that someone will make the mistake to mix this up. | ||
* [[Invariant Avoidance Principle]] (IAP): Invariants are statements that have t be true in order to keep a module in a consistent state. ML states that eventually an invariant will be broken resulting in a hard to detect defect. IAP states that invariants should therefore be avoided. So IAP is the application of ML to invariants. | * [[Invariant Avoidance Principle]] (IAP): Invariants are statements that have t be true in order to keep a module in a consistent state. ML states that eventually an invariant will be broken resulting in a hard to detect defect. IAP states that invariants should therefore be avoided. So IAP is the application of ML to invariants. | ||
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==== Contrary Principles ==== | ==== Contrary Principles ==== | ||
principles/murphy_s_law.txt · Last modified: 2021-10-20 21:18 by christian