To get a trial key
fill out the form below
Team License (a basic version)
Enterprise License (an extended version)
* By clicking this button you agree to our Privacy Policy statement

Request our prices
New License
License Renewal
--Select currency--
* By clicking this button you agree to our Privacy Policy statement

Free PVS-Studio license for Microsoft MVP specialists
* By clicking this button you agree to our Privacy Policy statement

To get the licence for your open-source project, please fill out this form
* By clicking this button you agree to our Privacy Policy statement

I am interested to try it on the platforms:
* By clicking this button you agree to our Privacy Policy statement

Message submitted.

Your message has been sent. We will email you at

If you haven't received our response, please do the following:
check your Spam/Junk folder and click the "Not Spam" button for our message.
This way, you won't miss messages from our team in the future.

Source Lines of Code

Source Lines of Code

Jul 15 2013

Source Lines of Code (SLOC) is a software metric frequently used to measure the size and complexity of a software project. It is typically used to predict the amount of effort and time that will be required to develop a program, as well as to estimate the programming productivity once the software is produced.

Physical and Logical SLOC

There are two major types of SLOC measures: physical SLOC and logical SLOC. Specific definitions of these terms vary depending on particular circumstances. The most common definition of physical SLOC is a count of lines in the text of the program's source code including comment lines and, sometimes, blank lines. Logical SLOC attempts to measure the number of executable expressions (such as operators, functions, etc.), but their specific definitions are tied to specific computer languages.

Therefore, each approach has its own strong and weak points: physical SLOC is easier to measure, but it is very sensitive to coding style conventions and code formatting, while logical SLOC is less sensitive to these factors yet not so easy to measure.

An Example of SLOC Measurement

Have a look at this code:

for (i=0; i<100; ++i) printf("%d bottles of beer on the wall\n");
//How many LOCs is here?

It has 2 physical SLOC, 2 logical SLOC (the loop operator for and the function call operator printf) and 1 comment line.

Now let's change the code formatting in the following way:

for (i=0; i<100; ++i)
    printf("%d bottles of beer on the wall\n ");
//How many LOCs is here?

We've got 5 physical SLOC and the same 2 logical SLOC and 1 comment line.

SLOC and Software Properties

The SLOC metric is obviously associated with the system complexity: the larger the code's size, the more complex the system is. For instance, SLOC for Windows NT 3.1 is about 4-5 million and 45 million for Windows XP; Linux kernel 2.6 has 5.6 million SLOC, and Linux kernel 3.6 has 15.9 million SLOC.

However, it's not as definite in case of software quality and reliability. Every real-life software product contains bugs, and the tendency is that larger programs have more bugs. The point becomes pretty clear when we introduce the "bugs/SLOC" ratio: even if it remains constant, the absolute quantity of bugs grows alongside with the program's size. Intuition tells us that it happens due to the rising system complexity (A. Tanenbaum). And not just intuition (see diagram: "typical error density"). This consideration underlies such development principles as KISS, DRY and SOLID. To support this idea, let me quote a meaningful phrase by the classic E. Dijkstra: "Simplicity is prerequisite for reliability", and a paragraph from his work "The Fruits of Misunderstanding":

...Yet people talk about programming as if it were a production process and measure "programmer productivity" in terms of "number of lines of code produced". In so doing they book that number on the wrong side of the ledger: we should always refer to "the number of lines of code spent".


Thus, we have found out that a software project grows in complexity when growing in size (the SLOC measure), which leads to more bugs. Unfortunately (or otherwise), the technological progress is unstoppable, and computer systems' complexity will go on to grow, requiring ever more resources to find and fix bugs (not without adding new ones at the same time, of course), that's why developers should consider using the static analysis methodology and specialized static analysis tools to reduce the number of bugs and enhance the efficiency of the development process in general.


Popular related articles
PVS-Studio ROI

Date: Jan 30 2019

Author: Andrey Karpov

Occasionally, we're asked a question, what monetary value the company will receive from using PVS-Studio. We decided to draw up a response in the form of an article and provide tables, which will sho…
The Last Line Effect

Date: May 31 2014

Author: Andrey Karpov

I have studied many errors caused by the use of the Copy-Paste method, and can assure you that programmers most often tend to make mistakes in the last fragment of a homogeneous code block. I have ne…
Characteristics of PVS-Studio Analyzer by the Example of EFL Core Libraries, 10-15% of False Positives

Date: Jul 31 2017

Author: Andrey Karpov

After I wrote quite a big article about the analysis of the Tizen OS code, I received a large number of questions concerning the percentage of false positives and the density of errors (how many erro…
How PVS-Studio Proved to Be More Attentive Than Three and a Half Programmers

Date: Oct 22 2018

Author: Andrey Karpov

Just like other static analyzers, PVS-Studio often produces false positives. What you are about to read is a short story where I'll tell you how PVS-Studio proved, just one more time, to be more atte…
Technologies used in the PVS-Studio code analyzer for finding bugs and potential vulnerabilities

Date: Nov 21 2018

Author: Andrey Karpov

A brief description of technologies used in the PVS-Studio tool, which let us effectively detect a large number of error patterns and potential vulnerabilities. The article describes the implementati…
The Ultimate Question of Programming, Refactoring, and Everything

Date: Apr 14 2016

Author: Andrey Karpov

Yes, you've guessed correctly - the answer is "42". In this article you will find 42 recommendations about coding in C++ that can help a programmer avoid a lot of errors, save time and effort. The au…
Appreciate Static Code Analysis!

Date: Oct 16 2017

Author: Andrey Karpov

I am really astonished by the capabilities of static code analysis even though I am one of the developers of PVS-Studio analyzer myself. The tool surprised me the other day as it turned out to be sma…
Static analysis as part of the development process in Unreal Engine

Date: Jun 27 2017

Author: Andrey Karpov

Unreal Engine continues to develop as new code is added and previously written code is changed. What is the inevitable consequence of ongoing development in a project? The emergence of new bugs in th…
The way static analyzers fight against false positives, and why they do it

Date: Mar 20 2017

Author: Andrey Karpov

In my previous article I wrote that I don't like the approach of evaluating the efficiency of static analyzers with the help of synthetic tests. In that article, I give the example of a code fragment…
Free PVS-Studio for those who develops open source projects

Date: Dec 22 2018

Author: Andrey Karpov

On the New 2019 year's eve, a PVS-Studio team decided to make a nice gift for all contributors of open-source projects hosted on GitHub, GitLab or Bitbucket. They are given free usage of PVS-Studio s…

Comments (0)

Next comments
This website uses cookies and other technology to provide you a more personalized experience. By continuing the view of our web-pages you accept the terms of using these files. If you don't want your personal data to be processed, please, leave this site.
Learn More →