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Sequences of duplicate code are sometimes known as code clones or just clones, the automated process of finding duplications in source code is called clone detection. Two code sequences may be duplicates of each other without being character-for-character identical, for example by being character-for-character identical only when white space ...
The Abstraction-Filtration-Comparison test (AFC) is a method of identifying substantial similarity for the purposes of applying copyright law. In particular, the AFC test is used to determine whether non-literal elements of a computer program have been copied by comparing the protectable elements of two programs.
The Jaccard similarity coefficient is a commonly used indicator of the similarity between two sets. Let U be a set and A and B be subsets of U, then the Jaccard index is defined to be the ratio of the number of elements of their intersection and the number of elements of their union:
The Computer Language Benchmarks Game site warns against over-generalizing from benchmark data, but contains a large number of micro-benchmarks of reader-contributed code snippets, with an interface that generates various charts and tables comparing specific programming languages and types of tests. [56]
As such, enumerations are one area where tools designed to automatically translate code between the two languages (such as Java to C# converters) fail. C# has implemented enumerations in a manner similar to C, that is as wrappers around the bit-flags implemented in primitive integral types (int, byte, short, etc.).
Additionally, in C# if a block consists of only a single statement, the braces may be omitted. C# is case sensitive while Visual Basic .NET is not. Thus in C# it is possible to have two variables with the same apparent name, for example variable1 and Variable1. Visual Studio will correct (make uniform) the case of variables as they are typed in ...
The higher the Jaro–Winkler distance for two strings is, the less similar the strings are. The score is normalized such that 0 means an exact match and 1 means there is no similarity. The original paper actually defined the metric in terms of similarity, so the distance is defined as the inversion of that value (distance = 1 − similarity).
Fuzzy hashing exists to solve this problem of detecting data that is similar, but not exactly the same, as other data. Fuzzy hashing algorithms specifically use algorithms in which two similar inputs will generate two similar hash values. This property is the exact opposite of the avalanche effect desired in cryptographic hash functions.