Source lines of code (SLOC), also known as lines of code (LOC), is a software metric used to measure the size of a computer program by counting the number of lines in the text of the program's source code. SLOC is typically used to predict the amount of effort that will be required to develop a program, as well as to estimate programming productivity or maintainability once the software is produced.
Measurement methods Multiple useful comparisons involve only the order of magnitude of lines of code in a project. Using lines of code to compare a 10,000-line project to a 100,000-line project is far more useful than when comparing a 20,000-line project with a 21,000-line project. While it is debatable exactly how to measure lines of code, discrepancies of an order of magnitude can be clear indicators of software complexity or man-hours. There are two major types of SLOC measures: physical SLOC (LOC) and logical SLOC (LLOC). Specific definitions of these two measures vary, but the most common definition of physical SLOC is a count of lines in the text of the program's source code excluding comment lines. Logical SLOC attempts to measure the number of executable "statements", but their specific definitions are tied to specific computer languages (one simple logical SLOC measure for C-like programming languages is the number of statement-terminating semicolons). It is much easier to create tools that measure physical SLOC, and physical SLOC definitions are easier to explain. However, physical SLOC measures are more sensitive to logically irrelevant formatting and style conventions than logical SLOC. However, SLOC measures are often stated without giving their definition, and logical SLOC can often be significantly different from physical SLOC. Consider this snippet of C code as an example of the ambiguity encountered when determining SLOC:
In this example we have:
1 physical line of code (LOC), 2 logical lines of code (LLOC) (for statement and printf statement), 1 comment line. Depending on the programmer and coding standards, the above "line" of code could be written on multiple separate lines:
In this example we have:
4 physical lines of code (LOC): is placing braces work to be estimated? 2 logical lines of code (LLOC): what about all the work writing non-statement lines? 1 comment line: tools must account for all code and comments regardless of comment placement. Even the "logical" and "physical" SLOC values can have a large number of varying definitions. Robert E. Park (while at the Software Engineering Institute) and others developed a framework for defining SLOC values, to enable people to carefully explain and define the SLOC measure used in a project. For example, most software systems reuse code, and determining which (if any) reused code to include is important when reporting a measure.
Origins At the time when SLOC was introduced as a metric, the most commonly used languages, such as FORTRAN and assembly language, were line-oriented languages. These languages were developed at the time when punched cards were the main form of data entry for programming. One punched card usually represented one line of code. It was one discrete object that was easily counted. It was the visible output of the programmer, so it made sense to managers to count lines of code as a measurement of a programmer's productivity, even referring to such as "card images". Today, the most commonly used computer languages allow a lot more leeway for formatting. Text lines are no longer limited to 80 or 96 columns, and one line of text no longer necessarily corresponds to one line of code.
Usage of SLOC measures
SLOC measures are somewhat controversial, particularly in the way that they are sometimes misused. Experiments have repeatedly confirmed that effort is highly correlated with SLOC, that is, programs with larger SLOC values take more time to develop. Thus, SLOC can be effective in estimating effort. However, functionality is less well correlated with SLOC: skilled developers may be able to develop the same functionality with far less code, so one program with fewer SLOC may exhibit more functionality than another similar program. Counting SLOC as productivity measure has its caveats, since a developer can develop only a few lines and yet be far more productive in terms of functionality than a developer who ends up creating more lines (and generally spending more effort). Good developers may merge multiple code modules into a single module, improving the system yet appearing to have negative productivity because they remove code. Furthermore, inexperienced developers often resort to code duplication, which is highly discouraged as it is more bug-prone and costly to maintain, but it results in higher SLOC. SLOC counting exhibits further accuracy issues at comparing programs written in different languages unless adjustment factors are applied to normalize languages. Various computer languages balance brevity and clarity in different ways; as an extreme example, most assembly languages would require hundreds of lines of code to perform the same task as a few characters in APL. The following example shows a comparison of a "hello world" program written in BASIC, C, and COBOL (a language known for being particularly verbose).
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