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In computer programming, duplicate code is a sequence of source code that occurs more than once, either within a program or across different programs owned or maintained by the same entity. Duplicate code is generally considered undesirable for a number of reasons. [ 1 ]
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
Refactoring is usually motivated by noticing a code smell. [2] For example, the method at hand may be very long, or it may be a near duplicate of another nearby method. Once recognized, such problems can be addressed by refactoring the source code, or transforming it into a new form that behaves the same as before but that no longer "smells".
Python FORTRAN, Jovial, Pascal, VHDL, HTML, PHP, XML A multi-platform tool for code analysis and comprehension of large code bases. Can recognize multiple dialects of C, C++ and C# like ANSI, K&R and Objective C++. Visual Expert: 2021-09-10 No; proprietary — — — — — — PowerBuilder, Oracle PL/SQL, SQL Server Transact-SQL (T-SQL)
A quine's output is exactly the same as its source code. A quine is a computer program that takes no input and produces a copy of its own source code as its only output. The standard terms for these programs in the computability theory and computer science literature are "self-replicating programs", "self-reproducing programs", and "self-copying programs".
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.
dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]