Version Control

Version Control

In software engineering, version control (also known as revision controlsource control, or source code management) is a class of systems responsible for managing changes to computer programs, documents, large web sites, or other collections of information. Version control is a component of software configuration management.[1]

Changes are usually identified by a number or letter code, termed the “revision number”, “revision level”, or simply “revision”. For example, an initial set of files is “revision 1”. When the first change is made, the resulting set is “revision 2”, and so on. Each revision is associated with a timestamp and the person making the change. Revisions can be compared, restored, and with some types of files, merged.

The need for a logical way to organize and control revisions has existed for almost as long as writing has existed, but revision control became much more important, and complicated, when the era of computing began. The numbering of book editions and of specification revisions are examples that date back to the print-only era. Today, the most capable (as well as complex) revision control systems are those used in software development, where a team of people may concurrently make changes to the same files.

Version control systems (VCS) are most commonly run as stand-alone applications, but revision control is also embedded in various types of software such as word processors and spreadsheets, collaborative web docs[2] and in various content management systems, e.g., Wikipedia’s page history. Revision control allows for the ability to revert a document to a previous revision, which is critical for allowing editors to track each other’s edits, correct mistakes, and defend against vandalism and spamming in wikis.

Overview[edit]

In computer software engineering, revision control is any kind of practice that tracks and provides control over changes to source code. Software developers sometimes use revision control software to maintain documentation and configuration files as well as source code.

As teams design, develop and deploy software, it is common for multiple versions of the same software to be deployed in different sites and for the software’s developers to be working simultaneously on updates. Bugs or features of the software are often only present in certain versions (because of the fixing of some problems and the introduction of others as the program develops). Therefore, for the purposes of locating and fixing bugs, it is vitally important to be able to retrieve and run different versions of the software to determine in which version(s) the problem occurs. It may also be necessary to develop two versions of the software concurrently: for instance, where one version has bugs fixed, but no new features (branch), while the other version is where new features are worked on (trunk).

At the simplest level, developers could simply retain multiple copies of the different versions of the program, and label them appropriately. This simple approach has been used in many large software projects. While this method can work, it is inefficient as many near-identical copies of the program have to be maintained. This requires a lot of self-discipline on the part of developers and often leads to mistakes. Since the code base is the same, it also requires granting read-write-execute permission to a set of developers, and this adds the pressure of someone managing permissions so that the code base is not compromised, which adds more complexity. Consequently, systems to automate some or all of the revision control process have been developed. This ensures that the majority of management of version control steps is hidden behind the scenes.

Moreover, in software development, legal and business practice, and other environments, it has become increasingly common for a single document or snippet of code to be edited by a team, the members of which may be geographically dispersed and may pursue different and even contrary interests. Sophisticated revision control that tracks and accounts for ownership of changes to documents and code may be extremely helpful or even indispensable in such situations.

Revision control may also track changes to configuration files, such as those typically stored in /etc or /usr/local/etc on Unix systems. This gives system administrators another way to easily track changes made and a way to roll back to earlier versions should the need arise.

History[edit]

IBM’s OS/360 IEBUPDTE software update tool dates back to 1962, arguably a precursor to VCS tools. A full system designed for source code control was started in 1972, SCCS for the same system (OS/360). SCCS’ introduction, having been published on December 4, 1975, historically implied it was the first deliberate revision control system.[3] RCS followed just after,[4] with its networked version CVS. The next generation after CVS was dominated by Subversion,[5] followed by the rise of distributed revision control tools such as Git.[6]

Structure[edit]

Revision control manages changes to a set of data over time. These changes can be structured in various ways.

Often the data is thought of as a collection of many individual items, such as files or documents, and changes to individual files are tracked. This accords with intuitions about separate files but causes problems when identity changes, such as during renaming, splitting or merging of files. Accordingly, some systems such as Git, instead consider changes to the data as a whole, which is less intuitive for simple changes but simplifies more complex changes.

When data that is under revision control is modified, after being retrieved by checking out, this is not in general immediately reflected in the revision control system (in the repository), but must instead be checked in or committed. A copy outside revision control is known as a “working copy”. As a simple example, when editing a computer file, the data stored in memory by the editing program is the working copy, which is committed by saving. Concretely, one may print out a document, edit it by hand, and only later manually input the changes into a computer and save it. For source code control, the working copy is instead a copy of all files in a particular revision, generally stored locally on the developer’s computer;[note 1] in this case saving the file only changes the working copy, and checking into the repository is a separate step.

If multiple people are working on a single data set or document, they are implicitly creating branches of the data (in their working copies), and thus issues of merging arise, as discussed below. For simple collaborative document editing, this can be prevented by using file locking or simply avoiding working on the same document that someone else is working on.

Revision control systems are often centralized, with a single authoritative data store, the repository, and check-outs and check-ins done with reference to this central repository. Alternatively, in distributed revision control, no single repository is authoritative, and data can be checked out and checked into any repository. When checking into a different repository, this is interpreted as a merge or patch.

Graph structure[edit]

Example history graph of a revision-controlled project; trunk is in green, branches in yellow, and graph is not a tree due to presence of merges (the red arrows).

In terms of graph theory, revisions are generally thought of as a line of development (the trunk) with branches off of this, forming a directed tree, visualized as one or more parallel lines of development (the “mainlines” of the branches) branching off a trunk. In reality the structure is more complicated, forming a directed acyclic graph, but for many purposes “tree with merges” is an adequate approximation.

Revisions occur in sequence over time, and thus can be arranged in order, either by revision number or timestamp.[note 2] Revisions are based on past revisions, though it is possible to largely or completely replace an earlier revision, such as “delete all existing text, insert new text”. In the simplest case, with no branching or undoing, each revision is based on its immediate predecessor alone, and they form a simple line, with a single latest version, the “HEAD” revision or tip. In graph theory terms, drawing each revision as a point and each “derived revision” relationship as an arrow (conventionally pointing from older to newer, in the same direction as time), this is a linear graph. If there is branching, so multiple future revisions are based on a past revision, or undoing, so a revision can depend on a revision older than its immediate predecessor, then the resulting graph is instead a directed tree (each node can have more than one child), and has multiple tips, corresponding to the revisions without children (“latest revision on each branch”).[note 3] In principle the resulting tree need not have a preferred tip (“main” latest revision) – just various different revisions – but in practice one tip is generally identified as HEAD. When a new revision is based on HEAD, it is either identified as the new HEAD, or considered a new branch.[note 4] The list of revisions from the start to HEAD (in graph theory terms, the unique path in the tree, which forms a linear graph as before) is the trunk or mainline.[note 5] Conversely, when a revision can be based on more than one previous revision (when a node can have more than one parent), the resulting process is called a merge, and is one of the most complex aspects of revision control. This most often occurs when changes occur in multiple branches (most often two, but more are possible), which are then merged into a single branch incorporating both changes. If these changes overlap, it may be difficult or impossible to merge, and require manual intervention or rewriting.

In the presence of merges, the resulting graph is no longer a tree, as nodes can have multiple parents, but is instead a rooted directed acyclic graph (DAG). The graph is acyclic since parents are always backwards in time, and rooted because there is an oldest version. However, assuming that there is a trunk, merges from branches can be considered as “external” to the tree – the changes in the branch are packaged up as a patch, which is applied to HEAD (of the trunk), creating a new revision without any explicit reference to the branch, and preserving the tree structure. Thus, while the actual relations between versions form a DAG, this can be considered a tree plus merges, and the trunk itself is a line.

In distributed revision control, in the presence of multiple repositories these may be based on a single original version (a root of the tree), but there need not be an original root, and thus only a separate root (oldest revision) for each repository, for example, if two people starting working on a project separately. Similarly in the presence of multiple data sets (multiple projects) that exchange data or merge, there isn’t a single root, though for simplicity one may think of one project as primary and the other as secondary, merged into the first with or without its own revision history.

Specialized strategies[edit]

Engineering revision control developed from formalized processes based on tracking revisions of early blueprints or bluelines[citation needed]. This system of control implicitly allowed returning to an earlier state of the design, for cases in which an engineering dead-end was reached in the development of the design. A revision table was used to keep track of the changes made. Additionally, the modified areas of the drawing were highlighted using revision clouds.

Version control is widespread in business and law. Indeed, “contract redline” and “legal blackline” are some of the earliest forms of revision control,[7] and are still employed in business and law with varying degrees of sophistication. The most sophisticated techniques are beginning to be used for the electronic tracking of changes to CAD files (see product data management), supplanting the “manual” electronic implementation of traditional revision control.[citation needed]

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