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The variation and complexity of requirement documentation make it a proven challenge. Requirements may be implicit and hard to uncover. It is difficult to know exactly how much and what kind of documentation is needed and how much can be left to the architecture and design documentation, and it is difficult to know how to document requirements considering the variety of people who shall read and use the documentation. Thus, requirements documentation is often incomplete . Without proper requirements documentation, software changes become more difficult — and therefore more error prone and time-consuming .
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The need for requirements documentation is typically related to the complexity of the product, the impact of the product, and the life expectancy of the software. If the software is very complex or developed by many people , requirements can help better communicate what to achieve. If the software is safety-critical and can have a negative impact on human life , more formal requirements documentation is often required. If the software is expected to live for only a month or two very little requirements documentation may be needed. If the software is a first release that is later built upon, requirements documentation is very helpful when managing the change of the software and verifying that nothing has been broken in the software when it is modified.
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Traditionally, requirements are specified in requirements documents . To manage the increased complexity and changing nature of requirements documentation , database-centric systems and special-purpose requirements management tools are advocated.
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In Agile software development, requirements are often expressed as User Stories with accompanying acceptance criteria.
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Architecture documentation is a special type of design document. In a way, architecture documents are third derivative from the code . Very little in the architecture documents is specific to the code itself. These documents do not describe how to program a particular routine, or even why that particular routine exists in the form that it does, but instead merely lays out the general requirements that would motivate the existence of such a routine. A good architecture document is short on details but thick on explanation. It may suggest approaches for lower level design, but leave the actual exploration trade studies to other documents.
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Another type of design document is the comparison document, or trade study. This would often take the form of a whitepaper. It focuses on one specific aspect of the system and suggests alternate approaches. It could be at the user interface, code, design, or even architectural level. It will outline what the situation is, describe one or more alternatives, and enumerate the pros and cons of each. A good trade study document is heavy on research, expresses its idea clearly , and most importantly is impartial. It should honestly and clearly explain the costs of whatever solution it offers as best. The objective of a trade study is to devise the best solution, rather than to push a particular point of view. It is perfectly acceptable to state no conclusion, or to conclude that none of the alternatives are sufficiently better than the baseline to warrant a change. It should be approached as a scientific endeavor, not as a marketing technique.
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A very important part of the design document in enterprise software development is the Database Design Document . It contains Conceptual, Logical, and Physical Design Elements. The DDD includes the formal information that the people who interact with the database need. The purpose of preparing it is to create a common source to be used by all players within the scene. The potential users are:
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When talking about Relational Database Systems, the document should include following parts:
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It is very important to include all information that is to be used by all actors in the scene. It is also very important to update the documents as any change occurs in the database as well.
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It is important for the code documents associated with the source code to be thorough, but not so verbose that it becomes overly time-consuming or difficult to maintain them. Various how-to and overview documentation guides are commonly found specific to the software application or software product being documented by API writers. This documentation may be used by developers, testers, and also end-users. Today, a lot of high-end applications are seen in the fields of power, energy, transportation, networks, aerospace, safety, security, industry automation, and a variety of other domains. Technical documentation has become important within such organizations as the basic and advanced level of information may change over a period of time with architecture changes. There is evidence that the existence of good code documentation actually reduces maintenance costs for software.
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Code documents are often organized into a reference guide style, allowing a programmer to quickly look up an arbitrary function or class.
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Often, tools such as Doxygen, NDoc, Visual Expert, Javadoc, JSDoc, EiffelStudio, Sandcastle, ROBODoc, POD, TwinText, or Universal Report can be used to auto-generate the code documents—that is, they extract the comments and software contracts, where available, from the source code and create reference manuals in such forms as text or HTML files.
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The idea of auto-generating documentation is attractive to programmers for various reasons. For example, because it is extracted from the source code itself , the programmer can write it while referring to the code, and use the same tools used to create the source code to make the documentation. This makes it much easier to keep the documentation up-to-date.
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A possible downside is that only programmers can edit this kind of documentation, and it depends on them to refresh the output . Some would characterize this as a pro rather than a con.
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Respected computer scientist Donald Knuth has noted that documentation can be a very difficult afterthought process and has advocated literate programming, written at the same time and location as the source code and extracted by automatic means. The programming languages Haskell and CoffeeScript have built-in support for a simple form of literate programming, but this support is not widely used.
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Elucidative Programming is the result of practical applications of Literate Programming in real programming contexts. The Elucidative paradigm proposes that source code and documentation be stored separately.
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Often, software developers need to be able to create and access information that is not going to be part of the source file itself. Such annotations are usually part of several software development activities, such as code walks and porting, where third-party source code is analysed in a functional way. Annotations can therefore help the developer during any stage of software development where a formal documentation system would hinder progress.
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Unlike code documents, user documents simply describe how a program is used.
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In the case of a software library, the code documents and user documents could in some cases be effectively equivalent and worth conjoining, but for a general application this is not often true.
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Typically, the user documentation describes each feature of the program, and assists the user in realizing these features. It is very important for user documents to not be confusing, and for them to be up to date. User documents do not need to be organized in any particular way, but it is very important for them to have a thorough index. Consistency and simplicity are also very valuable. User documentation is considered to constitute a contract specifying what the software will do. API Writers are very well accomplished towards writing good user documents as they would be well aware of the software architecture and programming techniques used. See also technical writing.
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User documentation can be produced in a variety of online and print formats. However, there are three broad ways in which user documentation can be organized.
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Tutorial: A tutorial approach is considered the most useful for a new user, in which they are guided through each step of accomplishing particular tasks.
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Thematic: A thematic approach, where chapters or sections concentrate on one particular area of interest, is of more general use to an intermediate user. Some authors prefer to convey their ideas through a knowledge based article to facilitate the user needs. This approach is usually practiced by a dynamic industry, such as Information technology.
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List or Reference: The final type of organizing principle is one in which commands or tasks are simply listed alphabetically or logically grouped, often via cross-referenced indexes. This latter approach is of greater use to advanced users who know exactly what sort of information they are looking for.
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A common complaint among users regarding software documentation is that only one of these three approaches was taken to the near-exclusion of the other two. It is common to limit provided software documentation for personal computers to online help that give only reference information on commands or menu items. The job of tutoring new users or helping more experienced users get the most out of a program is left to private publishers, who are often given significant assistance by the software developer.
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Like other forms of technical documentation, good user documentation benefits from an organized process of development. In the case of user documentation, the process as it commonly occurs in industry consists of five steps:
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User analysis, the basic research phase of the process.
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Planning, or the actual documentation phase.
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Draft review, a self-explanatory phase where feedback is sought on the draft composed in the previous step.
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Usability testing, whereby the usability of the document is tested empirically.
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Editing, the final step in which the information collected in steps three and four is used to produce the final draft.
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For many applications it is necessary to have some promotional materials to encourage casual observers to spend more time learning about the product. This form of documentation has three purposes:
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To excite the potential user about the product and instill in them a desire for becoming more involved with it.
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To inform them about what exactly the product does, so that their expectations are in line with what they will be receiving.
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To explain the position of this product with respect to other alternatives.
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"The resistance to documentation among developers is well known and needs no emphasis."
This situation is particularly prevalent in agile software development because these methodologies try to avoid any unnecessary activities that do not directly bring value.
Specifically, the Agile Manifesto advocates valuing "working software over comprehensive documentation", which could be interpreted cynically as "We want to spend all our time coding. Remember, real programmers don't write documentation."
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A survey among software engineering experts revealed, however, that documentation is by no means considered unnecessary in agile development.
Yet it is acknowledged that there are motivational problems in development, and that documentation methods tailored to agile development may be needed.
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Data exists in three states: data at rest, data in transit and data in use. Data within a computer, in most cases, moves as parallel data. Data moving to or from a computer, in most cases, moves as serial data. Data sourced from an analog device, such as a temperature sensor, may be converted to digital using an analog-to-digital converter. Data representing quantities, characters, or symbols on which operations are performed by a computer are stored and recorded on magnetic, optical, electronic, or mechanical recording media, and transmitted in the form of digital electrical or optical signals. Data pass in and out of computers via peripheral devices.
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Physical computer memory elements consist of an address and a byte/word of data storage. Digital data are often stored in relational databases, like tables or SQL databases, and can generally be represented as abstract key/value pairs. Data can be organized in many different types of data structures, including arrays, graphs, and objects. Data structures can store data of many different types, including numbers, strings and even other data structures.
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Metadata helps translate data to information. Metadata is data about the data. Metadata may be implied, specified or given.
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Data relating to physical events or processes will have a temporal component. This temporal component may be implied. This is the case when a device such as a temperature logger receives data from a temperature sensor. When the temperature is received it is assumed that the data has a temporal reference of now. So the device records the date, time and temperature together. When the data logger communicates temperatures, it must also report the date and time as metadata for each temperature reading.
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Fundamentally, computers follow a sequence of instructions they are given in the form of data. A set of instructions to perform a given task is called a program. A program is data in the form of coded instructions to control the operation of a computer or other machine. In the nominal case, the program, as executed by the computer, will consist of machine code. The elements of storage manipulated by the program, but not actually executed by the central processing unit , are also data. At its most essential, a single datum is a value stored at a specific location. Therefore, it is possible for computer programs to operate on other computer programs, by manipulating their programmatic data.
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To store data bytes in a file, they have to be serialized in a file format. Typically, programs are stored in special file types, different from those used for other data. Executable files contain programs; all other files are also data files. However, executable files may also contain data used by the program which is built into the program. In particular, some executable files have a data segment, which nominally contains constants and initial values for variables, both of which can be considered data.
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The line between program and data can become blurry. An interpreter, for example, is a program. The input data to an interpreter is itself a program, just not one expressed in native machine language. In many cases, the interpreted program will be a human-readable text file, which is manipulated with a text editor program. Metaprogramming similarly involves programs manipulating other programs as data. Programs like compilers, linkers, debuggers, program updaters, virus scanners and such use other programs as their data.
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For example, a user might first instruct the operating system to load a word processor program from one file, and then use the running program to open and edit a document stored in another file. In this example, the document would be considered data. If the word processor also features a spell checker, then the dictionary for the spell checker would also be considered data. The algorithms used by the spell checker to suggest corrections would be either machine code data or text in some interpretable programming language.
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In an alternate usage, binary files are sometimes called data as distinguished from human-readable text.
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The total amount of digital data in 2007 was estimated to be 281 billion gigabytes .
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Keys in data provide the context for values. Regardless of the structure of data, there is always a key component present. Keys in data and data-structures are essential for giving meaning to data values. Without a key that is directly or indirectly associated with a value, or collection of values in a structure, the values become meaningless and cease to be data. That is to say, there has to be a key component linked to a value component in order for it to be considered data.
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Data can be represented in computers in multiple ways, as per the following examples:
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The taxonomic rank-structure of classes, which is an example of a hierarchical data structure; and
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at run time, the creation of references to in-memory data-structures of objects that have been instantiated from a class library.
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It is only after instantiation that an object of a specified class exists. After an object's reference is cleared, the object also ceases to exist. The memory locations where the object's data was stored are garbage and are reclassified as unused memory available for reuse.
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In computer science, a library is a collection of read-only resources that is leveraged during software development to implement a computer program.
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Historically, a library consisted of subroutines . The concept now includes other forms of executable code including classes and non-executable data including images and text. It can also refer to a collection of source code.
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For example, a program could use a library to indirectly make system calls instead of making those system calls directly in the program.
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A library can be used by multiple, independent consumers . This differs from resources defined in a program which can usually only be used by that program.
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When a consumer uses a library resource, it gains the value of the library without having to implement it itself. Libraries encourage code reuse in a modular fashion.
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When writing code that uses a library, a programmer only needs to know high-level information such as what items it contains at and how to use the items – not all of the internal details of the library.
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Libraries can use other libraries resulting in a hierarchy of libraries in a program.
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A library of executable code has a well-defined interface by which the functionality is invoked.
For example, in C, a library function is invoked via C's normal function call capability. The linker generates code to call a function via the library mechanism if the function is available from a library instead of from the program itself.
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The functions of a library can be connected to the invoking program at different program lifecycle phases. If the code of the library is accessed during the build of the invoking program, then the library is called a static library. An alternative is to build the program executable to be separate from the library file. The library functions are connected after the executable is started, either at load-time or runtime. In this case, the library is called a dynamic library.
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Most compiled languages have a standard library, although programmers can also create their own custom libraries. Most modern software systems provide libraries that implement the majority of the system services. Such libraries have organized the services which a modern application requires. As such, most code used by modern applications is provided in these system libraries.
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The idea of a computer library dates back to the first computers created by Charles Babbage. An 1888 paper on his Analytical Engine suggested that computer operations could be punched on separate cards from numerical input. If these operation punch cards were saved for reuse then "by degrees the engine would have a library of its own."
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In 1947 Goldstine and von Neumann speculated that it would be useful to create a "library" of subroutines for their work on the IAS machine, an early computer that was not yet operational at that time. They envisioned a physical library of magnetic wire recordings, with each wire storing reusable computer code.
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Inspired by von Neumann, Wilkes and his team constructed EDSAC. A filing cabinet of punched tape held the subroutine library for this computer. Programs for EDSAC consisted of a main program and a sequence of subroutines copied from the subroutine library. In 1951 the team published the first textbook on programming, The Preparation of Programs for an Electronic Digital Computer, which detailed the creation and the purpose of the library.
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COBOL included "primitive capabilities for a library system" in 1959, but Jean Sammet described them as "inadequate library facilities" in retrospect.
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JOVIAL has a Communication Pool , roughly a library of header files.
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Another major contributor to the modern library concept came in the form of the subprogram innovation of FORTRAN. FORTRAN subprograms can be compiled independently of each other, but the compiler lacked a linker. So prior to the introduction of modules in Fortran-90, type checking between FORTRAN subprograms was impossible.
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By the mid 1960s, copy and macro libraries for assemblers were common. Starting with the popularity of the IBM System/360, libraries containing other types of text elements, e.g., system parameters, also became common.
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In IBM's OS/360 and its successors this is called a partitioned data set.
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The first object-oriented programming language, Simula, developed in 1965, supported adding classes to libraries via its compiler.
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Libraries are important in the program linking or binding process, which resolves references known as links or symbols to library modules. The linking process is usually automatically done by a linker or binder program that searches a set of libraries and other modules in a given order. Usually it is not considered an error if a link target can be found multiple times in a given set of libraries. Linking may be done when an executable file is created , or whenever the program is used at runtime .
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The references being resolved may be addresses for jumps and other routine calls. They may be in the main program, or in one module depending upon another. They are resolved into fixed or relocatable addresses by allocating runtime memory for the memory segments of each module referenced.
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Some programming languages use a feature called smart linking whereby the linker is aware of or integrated with the compiler, such that the linker knows how external references are used, and code in a library that is never actually used, even though internally referenced, can be discarded from the compiled application. For example, a program that only uses integers for arithmetic, or does no arithmetic operations at all, can exclude floating-point library routines. This smart-linking feature can lead to smaller application file sizes and reduced memory usage.
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Some references in a program or library module are stored in a relative or symbolic form which cannot be resolved until all code and libraries are assigned final static addresses. Relocation is the process of adjusting these references, and is done either by the linker or the loader. In general, relocation cannot be done to individual libraries themselves because the addresses in memory may vary depending on the program using them and other libraries they are combined with. Position-independent code avoids references to absolute addresses and therefore does not require relocation.
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When linking is performed during the creation of an executable or another object file, it is known as static linking or early binding. In this case, the linking is usually done by a linker, but may also be done by the compiler. A static library, also known as an archive, is one intended to be statically linked. Originally, only static libraries existed. Static linking must be performed when any modules are recompiled.
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All of the modules required by a program are sometimes statically linked and copied into the executable file. This process, and the resulting stand-alone file, is known as a static build of the program. A static build may not need any further relocation if virtual memory is used and no address space layout randomization is desired.
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A shared library or shared object is a file that is intended to be shared by executable files and further shared object files. Modules used by a program are loaded from individual shared objects into memory at load time or runtime, rather than being copied by a linker when it creates a single monolithic executable file for the program.
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Shared libraries can be statically linked during compile-time, meaning that references to the library modules are resolved and the modules are allocated memory when the executable file is created. But often linking of shared libraries is postponed until they are loaded.
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Although originally pioneered in the 1960s, dynamic linking did not reach the most commonly-used operating systems until the late 1980s. It was generally available in some form in most operating systems by the early 1990s. During this same period, object-oriented programming was becoming a significant part of the programming landscape. OOP with runtime binding requires additional information that traditional libraries do not supply. In addition to the names and entry points of the code located within, they also require a list of the objects they depend on. This is a side-effect of one of OOP's core concepts, inheritance, which means that parts of the complete definition of any method may be in different places. This is more than simply listing that one library requires the services of another: in a true OOP system, the libraries themselves may not be known at compile time, and vary from system to system.
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At the same time many developers worked on the idea of multi-tier programs, in which a "display" running on a desktop computer would use the services of a mainframe or minicomputer for data storage or processing. For instance, a program on a GUI-based computer would send messages to a minicomputer to return small samples of a huge dataset for display. Remote procedure calls already handled these tasks, but there was no standard RPC system.
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Soon the majority of the minicomputer and mainframe vendors instigated projects to combine the two, producing an OOP library format that could be used anywhere. Such systems were known as object libraries, or distributed objects, if they supported remote access . Microsoft's COM is an example of such a system for local use. DCOM, a modified version of COM, supports remote access.
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For some time object libraries held the status of the "next big thing" in the programming world. There were a number of efforts to create systems that would run across platforms, and companies competed to try to get developers locked into their own system. Examples include IBM's System Object Model , Sun Microsystems' Distributed Objects Everywhere , NeXT's Portable Distributed Objects , Digital's ObjectBroker, Microsoft's Component Object Model , and any number of CORBA-based systems.
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Class libraries are the rough OOP equivalent of older types of code libraries. They contain classes, which describe characteristics and define actions that involve objects. Class libraries are used to create instances, or objects with their characteristics set to specific values. In some OOP languages, like Java, the distinction is clear, with the classes often contained in library files and the instantiated objects residing only in memory . In others, like Smalltalk, the class libraries are merely the starting point for a system image that includes the entire state of the environment, classes and all instantiated objects.
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Today most class libraries are stored in a package repository . Client code explicitly declare the dependencies to external libraries in build configuration files .
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Another library technique uses completely separate executables and calls them using a remote procedure call over a network to another computer. This maximizes operating system re-use: the code needed to support the library is the same code being used to provide application support and security for every other program. Additionally, such systems do not require the library to exist on the same machine, but can forward the requests over the network.
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However, such an approach means that every library call requires a considerable amount of overhead. RPC calls are much more expensive than calling a shared library that has already been loaded on the same machine. This approach is commonly used in a distributed architecture that makes heavy use of such remote calls, notably client-server systems and application servers such as Enterprise JavaBeans.
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Code generation libraries are high-level APIs that can generate or transform byte code for Java. They are used by aspect-oriented programming, some data access frameworks, and for testing to generate dynamic proxy objects. They also are used to intercept field access.
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The system stores libfoo.a and libfoo.so files in directories such as /lib, /usr/lib or /usr/local/lib. The filenames always start with lib, and end with a suffix of .a or of .so . Some systems might have multiple names for a dynamically linked library. These names typically share the same prefix and have different suffixes indicating the version number. Most of the names are names for symbolic links to the latest version. For example, on some systems libfoo.so.2 would be the filename for the second major interface revision of the dynamically linked library libfoo. The .la files sometimes found in the library directories are libtool archives, not usable by the system as such.
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The system inherits static library conventions from BSD, with the library stored in a .a file, and can use .so-style dynamically linked libraries . Most libraries in macOS, however, consist of "frameworks", placed inside special directories called "bundles" which wrap the library's required files and metadata. For example, a framework called MyFramework would be implemented in a bundle called MyFramework.framework, with MyFramework.framework/MyFramework being either the dynamically linked library file or being a symlink to the dynamically linked library file in MyFramework.framework/Versions/Current/MyFramework.
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Dynamic-link libraries usually have the suffix *.DLL, although other file name extensions may identify specific-purpose dynamically linked libraries, e.g. *.OCX for OLE libraries. The interface revisions are either encoded in the file names, or abstracted away using COM-object interfaces. Depending on how they are compiled, *.LIB files can be either static libraries or representations of dynamically linkable libraries needed only during compilation, known as "import libraries". Unlike in the UNIX world, which uses different file extensions, when linking against .LIB file in Windows one must first know if it is a regular static library or an import library. In the latter case, a .DLL file must be present at runtime.
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A computer-aided design library or CAD library is a cloud based repository of 3D models or parts for computer-aided design , computer-aided engineering , computer-aided manufacturing , or Building information modeling . Examples of CAD libraries are GrabCAD, Sketchup 3D Warehouse, Sketchfab, McMaster-Carr, TurboSquid, Chaos Cosmos, and Thingiverse. The models can be free and open source or proprietary and have to pay a subscription to have access to the CAD library 3D models. Generative Ai CAD libraries are being developed using linked open data of schematics and diagrams.
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CAD libraries can have assets such as 3D models, materials/textures, bump maps, trees/plants, HDRIs, and different Computer graphics lighting sources to be rendered.
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Thus, these are different from distributed computing problems that need communication between tasks, especially communication of intermediate results. They are easy to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are thus well suited to large, Internet-based volunteer computing platforms such as BOINC, and do not suffer from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all.
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A common example of an embarrassingly parallel problem is 3D video rendering handled by a graphics processing unit, where each frame or pixel can be handled with no interdependency. Some forms of password cracking are another embarrassingly parallel task that is easily distributed on central processing units, CPU cores, or clusters.
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"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods." The term is first found in the literature in a 1986 book on multiprocessors by MATLAB's creator Cleve Moler, who claims to have invented the term.
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An alternative term, pleasingly parallel, has gained some use, perhaps to avoid the negative connotations of embarrassment in favor of a positive reflection on the parallelizability of the problems: "Of course, there is nothing embarrassing about these programs at all."
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Some examples of embarrassingly parallel problems include:
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Cryptography, or cryptology , is the practice and study of techniques for secure communication in the presence of adversarial behavior. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, information security, electrical engineering, digital signal processing, physics, and others. Core concepts related to information security are also central to cryptography. Practical applications of cryptography include electronic commerce, chip-based payment cards, digital currencies, computer passwords, and military communications.
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Cryptography prior to the modern age was effectively synonymous with encryption, converting readable information to unintelligible nonsense text , which can only be read by reversing the process . The sender of an encrypted message shares the decryption technique only with the intended recipients to preclude access from adversaries. The cryptography literature often uses the names "Alice" for the sender, "Bob" for the intended recipient, and "Eve" for the eavesdropping adversary. Since the development of rotor cipher machines in World War I and the advent of computers in World War II, cryptography methods have become increasingly complex and their applications more varied.
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