How to Apply the Single Responsibility Principle in your Code

Have you ever come across a class or method with so much going on that your head starts to spin?

The Single Responsibility Principle (SRP) is a best practice methodology that states that a function, method, or class should have one main specific purpose.

Having classes or functions which have a lot of purposes makes your code hard to read, understand, difficult to trace bugs, and hard to maintain in the long run.

Creating smaller classes, functions, or modules that are geared for some specific purpose will lead to more robust, and maintainable code. In addition, your code becomes easier to unit test.

In this guide, we will take a look at how we can write code that fulfills the Single Responsibility Principle by refactoring a component file that has several purposes into one that has a specific purpose.


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How to Apply the Single Responsibility Principle in your Code
Tyrique  Littel

Tyrique Littel


Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer


We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:


The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:



import io


import tokenize



code = b"color = input('Enter your favourite color: ')"



for token in tokenize.tokenize(io.BytesIO(code).readline):





TokenInfo(type=62 (ENCODING),  string='utf-8')


TokenInfo(type=1  (NAME),      string='color')


TokenInfo(type=54 (OP),        string='=')


TokenInfo(type=1  (NAME),      string='input')


TokenInfo(type=54 (OP),        string='(')


TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")


TokenInfo(type=54 (OP),        string=')')


TokenInfo(type=4  (NEWLINE),   string='')


TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Fix Your Code Using The Single-Responsibility Principle

The symptoms are mild at first, but a lack of awareness and ability to contain it immediately causes a rapid spread. No, I’m not talking about COVID-19, I’m talking about disorganized code. If it isn’t fixed it right away, it’ll affect everything after it. Luckily, convoluted code can be easily prevented by applying the single-responsibility principle.

Here’s what Wikipedia says the Single-responsibility principle means:

The single-responsibility principle (SRP) is a computer-programming principle that states that every module or class should have responsibility over a single part of the functionality provided by the software, and that responsibility should be entirely encapsulated by the class, module or function.

TL;DR: Each module, class, or function should do just one thing. In other words, separation of concerns or modularization.

Where The Single-Responsibility Principle Comes In Handy

Admittedly, if you’re writing a simple “Hello World” program, modularizing can be overkill. However, any code that goes deeper than that requires modularizing.

Even though HTML/CSS are not programming languages, it’s a great example to start with. Anyone who’s tried to make a website knows the pain of not modularizing. You try to move a section a couple of pixels to the left and all hell breaks loose. Your website shifts halfway down the screen and your navbar turns purple. Then you cry and reevaluate your life because all of your backend developer friends say that frontend work is easy.

Ok, maybe a bit of an exaggeration.

Image for post

Image for post

Source: xkcd

Nonetheless, the main takeaway here is that having a separation of concerns allows for modifying parts of your code without breaking the entire program.

I used to fear writing large programs because I knew that once my program got too large, it would have bugs that would cause more bugs when fixed. If I had just modularized then there wouldn’t have been anything to fear.

Where The Single-Responsibility Principle is Necessary

In general, the single-responsibility principle comes in useful for large applications. **Having separate functions for performing individual responsibilities allows for code reusability and easier testing. **It is much easier to test a program when you can call the individual functions and see their outputs rather than having to use the debugger to step through each line of the program until you find the bug.

For extremely large applications that may require several developers/teams, modularization is critical. Picture this: you are assigned to work with a different team on a specific feature. The codebase is a decade old, has over 10,000 lines, no documentation, and everything was written in one file. Good luck.

All of this could have been prevented if the programmers who created the mess applied the single-responsibility principle. Even without documentation and properly named variables and functions, simply modularizing can make a codebase significantly more readable.

Don’t be the guy who leaves spaghetti code for an unfortunate new grad/new hire to dig through. Write clean, modularized code. Your co-workers (or graders if you’re in college) will thank you.

Final Thoughts

Aside from general modularizing, another way to think about SRP is by asking yourself the following question: If I change one thing, how many other things will it impact?

As a rule of thumb, limit the impact of change. A useful metaphor to consider is a company like Tesla. While Elon Musk has an understanding of the hardware, assembly, and software for Tesla, he is not the one coding and putting together the cars. Similarly, your main method or app.js shouldn’t be handling all the work. Split it up into different classes and the smaller tasks into different functions. It will make your code easier to test, modify, and read. For more information, there is a great article on the single-responsibility principle by Robert C. Martin, the author of Clean Code.

#coding #programming #computer-science #code #clean-code

Samanta  Moore

Samanta Moore


Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

Houston  Sipes

Houston Sipes


How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash


  • Readability
  • Maintainability
  • Code Quality
  • Premature Optimization


  1. Refactor the code
  2. Use better names


  • Optimized loops


  • Optimized code for low-level operations.

Sample Code


function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       n /= d;
	  return f;


function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	    if(remainder % divisor === 0){
	       remainder = remainder/ divisor;
	  return factors;


Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

Fannie  Zemlak

Fannie Zemlak


Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

  • Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

  • Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

  • Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company