Source lines of code

From Wikipedia, the free encyclopedia

Source lines of code (SLOC) is a software metric used to measure the amount of code in a software program. SLOC is typically used to estimate the amount of effort that will be required to develop a program, as well as to estimate productivity or effort once the software is produced.

[edit] Measuring SLOC

Many useful comparisons involve only the order of magnitude of lines of code in a project. Software projects can vary between 100 to 100,000,000 lines of code. 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, wide discrepancies in 2 different measurements should not vary by an order of magnitude.

There are two major types of SLOC measures: physical SLOC and logical SLOC. 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 including comment lines. Blanks lines are also included unless the lines of code in a section consists of more than 25% blank lines. In this case blank lines in excess of 25% are not counted toward lines of code.

Logical SLOC measures attempt to measure the number of "statements", but their specific definitions are tied to specific computer languages (one simple logical SLOC measure for C-like 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 sensitive to logically irrelevant formatting and style conventions, while logical SLOC is less sensitive to formatting and style conventions. Unfortunately, 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:

for (i=0; i<100; ++i) printf("hello"); /* How many lines of code is this? */

In this example we have:

• 1 Physical Lines of Code LOC
• 2 Logical Lines of Code lLOC (for statement and printf statement)
• 1 Comment Line

Depending on the programmer and/or coding standards, the above "line of code" could be, and usually is, written on many separate lines:

for (i=0; i<100; ++i)
{
    printf("hello");
} /* Now how many lines of code is this? */

In this example we have:

• 4 Physical Lines of Code LOC (Is placing braces work to be estimated?)
• 2 Logical Line 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) et al. 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.

[edit] Origins of SLOC

At the time that people began using SLOC as a metric, the most commonly used languages, such as FORTRAN and assembler, were line-oriented languages. These languages were developed at the time when punch cards were the main form of data entry for programming. One punch 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. Today, the most commonly used computer languages allow a lot more leeway for formatting. One line of text no longer necessarily corresponds to one line of code.

[edit] 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 very 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 less SLOC may exhibit more functionality than another similar program. In particular, SLOC is a poor productivity measure of individuals, 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. Also, especially skilled developers tend to be assigned the most difficult tasks, and thus may sometimes appear less "productive" than other developers on a task by this measure.

SLOC is particularly ineffective 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 C, and the same program written in COBOL - a language known for being particularly verbose.

#include <stdio.h>

int main(void) {

   printf("Hello World");
   return 0;
}

Another increasingly common problem in comparing SLOC metrics is the difference between auto-generated and hand-written code. Modern software tools often have the capability to auto-generate enormous amounts of code with a few clicks of a mouse. For instance, GUI builders automatically generate all the source code for a GUI object simply by dragging an icon onto a workspace. The work involved in creating this code cannot reasonably be compared to the work necessary to write a device driver, for instance. By the same token, a hand-coded custom GUI class could easily be more demanding than a simple device driver; hence the shortcoming of this metric.

There are several cost, schedule, and effort estimation models which use SLOC as an input parameter, including the widely-used Constructive Cost Model (COCOMO) series of models by Barry Boehm et al, PRICE Systems True S and Galorath's SEER-SEM. While these models have shown good predictive power, they are only as good as the estimates (particularly the SLOC estimates) fed to them. Many have advocated the use of function points instead of SLOC as a measure of functionality, but since function points are highly correlated to SLOC (and cannot be automatically measured) this is not a universally held view.

According to Andrew Tanenbaum, the SLOC values for various operating systems in Microsoft's Windows NT product line are as follows:

David A. Wheeler studied the Red Hat distribution of the GNU/Linux operating system, and reported that Red Hat Linux version 7.1 (released April 2001) contained over 30 million physical SLOC. He also determined that, had it been developed by conventional proprietary means, it would have required about 8,000 person-years of development effort and would have cost over $1 billion (in year 2000 U.S. dollars).

A similar study was later made of Debian GNU/Linux version 2.2 (also known as "Potato"); this version of GNU/Linux was originally released in August 2000. This study found that Debian GNU/Linux 2.2 included over 55 million SLOC, and if developed in a conventional proprietary way would have required 14,005 person-years and cost $1.9 billion USD to develop. Later runs of the tools used report that the following release of Debian had 104 million SLOC, and as of year 2005, the newest release is going to include over 213 million SLOC.

One can find figures of major operating systems (the various Windows version have been presented in a table above)

In comparison, below are figures for various applications.

[edit] SLOC and relation to security faults

The central enemy of reliability is complexity.

—Geer et al

A number of experts have claimed a relationship between the number of lines of code in a program and the number of bugs that it contains. This relationship is not simple, since the number of errors per line of code varies greatly according to the language used, the type of quality assurance processes, and level of testing, but it does appear to exist. More importantly, the number of bugs in a program has been directly related to the number of security faults that are likely to be found in the program.

This has had a number of important implications for system security and these can be seen reflected in operating system design. Firstly, more complex systems are likely to be more insecure simply due to the greater number of lines of code needed to develop them. For this reason, security focused systems such as OpenBSD grow much more slowly than other systems such as Windows and Linux. A second idea, taken up in both OpenBSD and many Linux variants, is that separating code into different sections which run with different security environments (with or without special privileges, for example) ensures that the most security critical segments are small and carefully audited.

This article may require cleanup to meet Wikipedia's quality standards. Please discuss this issue on the talk page or replace this tag with a more specific message. This article has been tagged since July 2006.

[edit] Advantages

(a) Scope for Automation of Counting: Since Line of Code is a physical entity; manual counting effort can be easily eliminated by automating the counting process. Small utilities may be developed for counting the LOC in a program. However, a code counting utility developed for a specific language cannot be used for other languages due to the syntactical and structural differences among languages.

(b) An Intuitive Metric: Line of Code serves as an intuitive metric for measuring the size of software due to the fact that it can be seen and the effect of it can be visualized. Function Point is more of an objective metric which cannot be imagined as being a physical entity, it exists only in the logical space. This way, LOC comes in handy to express the size of software among programmers with low levels of experience.

[edit] Disadvantages

Measuring programming progress by lines of code is like measuring aircraft building progress by weight.

—Bill Gates

(a) Lack of Accountability: Lines of code measure suffers from some fundamental problems. Some think it isn't useful to measure the productivity of a project using only results from the coding phase, which usually accounts for only 30% to 35% of the overall effort.

(b) Lack of Cohesion with Functionality: Though experiments have repeatedly confirmed that effort is highly correlated with LOC, functionality is less well correlated with LOC. That is, skilled developers may be able to develop the same functionality with far less code, so one program with less LOC may exhibit more functionality than another similar program. In particular, LOC is a poor productivity measure of individuals, since a developer can develop only a few lines and still be more productive than a developer creating more lines of code.

(c) Adverse Impact on Estimation: As a consequence of the fact presented under point (a), estimates done based on lines of code can adversely go wrong, in all possibility.

(d) Developer’s Experience: Implementation of a specific logic differs based on the level of experience of the developer. Hence, number of lines of code differs from person to person. An experienced developer may implement certain functionality in fewer lines of code than another developer of relatively less experience does, though they use the same language.

(e) Difference in Languages: Consider two applications that provide the same functionality (screens, reports, databases). One of the applications is written in C++ and the other application written in a language like COBOL. The number of function points would be exactly the same, but aspects of the application would be different. The lines of code needed to develop the application would certainly not be the same. As a consequence, the amount of effort required to develop the application would be different (hours per function point). Unlike Lines of Code, the number of Function Points will remain constant.

(f) Advent of GUI Tools: With the advent of GUI-based languages/tools such as Visual Basic, much of development work is done by drag-and-drops and a few mouse clicks, where the programmer virtually writes no piece of code, most of the time. It is not possible to account for the code that is automatically generated in this case. This difference invites huge variations in productivity and other metrics with respect to different languages, making the Lines of Code more and more irrelevant in the context of GUI-based languages/tools, which are prominent in the present software development arena.

(g) Problems with Multiple Languages: In today’s software scenario, software is often developed in more than one language. Very often, a number of languages are employed depending on the complexity and requirements. Tracking and reporting of productivity and defect rates poses a serious problem in this case since defects cannot be attributed to a particular language subsequent to integration of the system. Function Point stands out to be the best measure of size in this case.

(h) Lack of Counting Standards: There is no standard definition of what a line of code is. Do comments count? Are data declarations included? What happens if a statement extends over several lines? – These are the questions that often arise. Though organizations like SEI and IEEE have published some guidelines in an attempt to standardize counting, it is difficult to put these into practice especially in the face of newer and newer languages being introduced every year.

[edit] Related terms

KLOC: 1000 lines of code

KDLOC: 1000 delivered lines of code

KSLOC: 1000 source lines of code

MLOC: 1000000 lines of code

GLOC: 1000000000 lines of code

TLOC: 1000000000000 lines of code

[edit] Programs for counting lines of code

There are many applications available for programmatically counting lines of code within source. Requirements for a source code metric tool should include the ability to process many source code languages and be operating system independent. Companies that use one tool for C on Windows and another tool for C on UNIX and a third tool for Java on Linux do not develop a common estimation basis for their CMMI metrics.

[edit] Free

• The simplest source line counting command in UNIX variants is wc. For example, to count the number of lines in all .cxx, .cpp, .h, and .c files in and below the current directory, use:

 find . -regex '.*\.\(c\|h\|cxx\|cpp\)' -print0 | xargs -0 cat | wc -l

• JCounter is a free open source Java program designed to be extensible and provide many statistics of source files. It is able to support any language or change to logical line counts by implementing a simple interface. Currently it supports Java and C++. JCounter can be found at the JCounter website
• LocMetrics is a free Windows tool for counting lines of C#, Java, or C++ code.
• SLOCCount, a tool available under GPL for counting lines of code for more than two dozen programming languages
• CCCC, another tool available under GPL which analyzes C++ and Java files and generates a report on various metrics of the code. Metrics supported include lines of code, McCabe's complexity and metrics proposed by Chidamber&Kemerer and Henry&Kafura.

[edit] Commercial

• EZ-Metrix is a commercial web-based source code counting utility that measures more than 75 different languages, and compares two file lists to quantify differences (i.e., new, modified, deleted, unmodified).
• Resource Standard Metrics is a commercial tool designed to process all popular languages while operating on Windows, UNIX, Linux and Mac OS X.
• A Windows program for counting KLOCs in source files is available at [1].
• Another program for Windows, Code Counter Pro, which counts physical KLOCs and supports languages like C, C++, C#, Java, Cobol, Delphi, VB, ASP, PHP and Fortran.

[edit] Cultural references

In the PBS documentary Triumph of the Nerds, Microsoft executive Steve Ballmer criticized the use of counting lines of code:

In IBM there's a religion in software that says you have to count K-LOCs, and a K-LOC is a thousand line of code. How big a project is it? Oh, it's sort of a 10K-LOC project. This is a 20K-LOCer. And this is 50K-LOCs. And IBM wanted to sort of make it the religion about how we got paid. How much money we made off OS/2, how much they did. How many K-LOCs did you do? And we kept trying to convince them - hey, if we have - a developer's got a good idea and he can get something done in 4K-LOCs instead of 20K-LOCs, should we make less money? Because he's made something smaller and faster, less K-LOC. K-LOCs, K-LOCs, that's the methodology. Ugh! Anyway, that always makes my back just crinkle up at the thought of the whole thing.

[edit] References

1. ^ González-Barahona, Jesús M., Miguel A. Ortuño Pérez, Pedro de las Heras Quirós, José Centeno González, and Vicente Matellán Olivera. Counting potatoes: the size of Debian 2.2. debian.org. Retrieved on 2003-08-12.
2. ^ ^{a b c} Robles, Gregorio. Debian Counting. Retrieved on 2007-02-16.
3. ^ Jobs, Steve (August 2006). Live from WWDC 2006: Steve Jobs Keynote. Retrieved on 2007-02-16. “86 million lines of source code that was ported to run on an entirely new architecture with zero hiccups.”

[edit] External Links

• Definitions of Practical Source Lines of Code Resource Standard Metrics (RSM) defines "effective lines of code" as a realistics code metric independent of programming style.
• Effective Lines of Code eLOC Metrics for popular Open Source Software Linux Kernel 2.6.17, Firefox, Apache HPPD, MySQL, PHP using RSM.
• McGraw, Gary (March/April 2003). "From the Ground Up: The DIMACS Software Security Workshop". IEEE Security & Privacy 1 (2): pp. 59-66.
• Park, Robert E., et. al.. "Software Size Measurement: A Framework for Counting Source Statements". Technical Report CMU/SEI-92-TR-20.
• Wheeler, David A.. SLOCCount. Retrieved on 2003-08-12.
• Wheeler, David A. (June 2001). More than a Gigabuck: Estimating GNU/Linux's Size. Retrieved on 2003-08-12.
• Tanenbaum, Andrew S. Modern Operating Systems (2nd ed.). Prentice Hall. ISBN 0-13-092641-8.
• Howard Dahdah (2007-01-24). Tanenbaum outlines his vision for a grandma-proof OS. Retrieved on 2007-01-29.
• line of Java Code: a plugin for Eclipse
• locmetrics:a list of different metrics tools and utilities