Counting lines of code can be used as one of the metrics to assess your developers' productivity and the efficiency of code bases. Therefore, in this blog, we dive into the different methods to count lines of code and what we think about this metric.Let’s dive in!
There are several ways to count LOC, some more efficient than others with different objectives. So we have a list for you to use the best method that suits your needs.
Counting lines of code manually involves going through each line of code and tallying the lines while excluding comments, blank spaces, and non-executable lines.To do this, open the source code file and count the lines with a paper or text editor. Go through it line by line and check for executable code.Once you are done, note the total count.However this method might sound simple, yet it is tedious and prone to errors. Automated tools are more accurate and efficient, and we will discuss them in the following methods.
The Count Lines of Code (CLOC) command is a tool that automates counting lines of code.Here's how to use the CLOC command:
Here is an example of the tool and its results.
The automated categorization of lines into different types and comprehensive statistics using CLOC makes it superior to manual counting. You can trust the reliability and efficiency of this tool to save you a lot of time and effort.
While the above methods show the lines of code, if your code increases in more blanks, comments, and statements, then the number gets inflated without any value added to the functionality.Therefore, statements count is much more helpful in understanding the executability and functional efficiency of the code.In languages like C, C++, C#, or Java, a statement ends with a semicolon (;). And in languages like basic and VB, several statements can be added with a colon (:). Thus, the way it is done differs, but the logic remains the same.The executability (XQT) is calculated by dividing the number of executable statements (STMTX) by all the statements (SMT).Here are some specifics of this method:
While this does provide code complexity information, it still needs to be a fool-proof method. Factors such as data dependency and error handling can hamper the executability of the code.
When measuring developer productivity, it's crucial to select the appropriate metrics. Simply counting lines of code may not be dependable. Instead, experts advise measuring the number of executable statements within the runtime environment, which is a more comprehensive and precise comprehension of progress.Here are the steps:
Following these simplified steps, you can easily count the lines of IL code in your program.
Counting lines of code has been a long-time metric; however, every tool and method gives different answers.Using it as a tool for productivity can be problematic because it is not comparable or fair unless you compare the same team’s code within the same team for the same code, language, and style.Here are some more points for you to consider:
LOC can give you glimpses into code complexity, code length, and executability, but that’s where its importance should stop. Typo’s CEO and Founder, Kshitij Mohan, says, “Measuring developer productivity solely by lines of code is like assessing a painting by its brushstrokes. It focuses on solution complexity rather than the complexity of the problem at hand. And like most metrics, it means very little without context.” Therefore, we believe you can count the lines of code all you want, but don’t use it as a metric to determine which code is better or which developer is more productive. Use it as intended – as a metric to help you along the way.