Common Software Engineering Practices For Production Code

In this article, we will learn about various software engineering practices common in the software industry today. This will help improve the standard of code, code quality, and practices which helps to develop a better system in a long-term view.

In a professional setting, when teams are building software for some specific client or developing an in-house project for the company itself, it is important to write clean, modular code. This will benefit them in numerous ways along with any easier modification for the future, thus future-proofing the system. Let us learn about different matters to take into concern.

Production Code

The production code is basically the software that runs on the production servers. These codes are responsible for everything that happens on the internet for the system which handles data and every interaction with the live users online.

Production-Quality Code

This is different from the Production Code. Unlike Production Code which actually goes in the production that is live, Production-Quality Code is the standard of code that needs to meet the expectation for production on the aspects of efficiency, reliability, modularity, and more.

Clean Code

Clean code is important in industry practice so that codes can be easily maintained even with a changing batch of engineers. Otherwise, it would be really difficult for new engineers to read and understand the code that was written years ago. An average engineer's tenure in a software company is 1.5 to 3 years before moving to a new company. Hence, with clean code, engineers can collaborate more easily within the team and also make it easier to maintain the code for the future. 

Tips for Clean Code

Differentiate in name the data types: fruit_list or fruit_dictionary will be more defining than using just fruit as a variable name. To learn more about Data Types and Common functions for it in Python, read the previous article: Commonly Used Python Functions For Data Structure 

Implicit Implication

While describing functions or variables, verbs and nouns respectively would make it more clear for collaborating team members while reading. Using prefixes such as is_ would intuitively imply conditions for Boolean values.

Make use of universal names

When working on a particular project, different engineers from full-stack engineering, machine learning, data scientist, and more come into play. If a data scientist uses a particular abbreviation only common to the data scientist field or machine learning engineer uses similar abbreviations and letters that are commonly understood to the group, it would be esoteric and diminish the understanding of other team members. Hence, it's crucial engineers stay away from using abbreviations and specific letters for personal ease.

Balance Naming Convention

A lot of newbie engineers take the descriptive process way too far and name long functions and highly specific uses. It is essential to realize, the naming should only be descriptive giving relative information rather than high-level specification such as too much nitty-gritty.

It might be uncommon knowledge that it often takes a level of effort to get into the habit of writing meaningful names for variables and functions. One way to realize and improve personal skills at this is to take feedback from your own team members. It's rather better to ask for reviews early on in your career than having to deal with unsolicited comments farther down the career path.

Proper Indentation

This is a monumental one. The view of a code that has been properly indented to those who aren’t following the PEP 8 style guideline is the difference between night and day. Every developer should organize their code with appropriate and consistent indentation with four space indentation which is standard practice. One of the ways to make this easier for future usage is by defaulting this in the text editor. Leaving out blank spaces and separating sections will help the reader breathe some air and will always look and feel nicer. Proper practice is to limit a line of code to 79 characters. This is a guideline in PEP 8 Style Guide. Read the PEP 8 Guideline to learn more.

Modular Code

Modular Code refers to the practice of breaking down code into modules and functions such that their logic remains intact. This makes code as it increases more re-useable, organized, and manageable.


Module in a code are segments of code that are divided into different individual files which can be encapsulated to use in other files through the process of importing. This process helps to break down a large codebase into the different smaller segments and easier to call for usage whenever needed.

Refactoring Code

Refactoring is the process of restructuring code in order to improve the internal structure of the code. During this process, the functionality of the code is unchanged. It is one of the crucial practices to push to production code as refactoring makes it easier to segment programs into modules and clean the code.

It is a known fact for experienced developers, the first written code isn’t always the best. Refactoring helps to bridge that flaw by turning it into the best code possible to produce high-quality code. It may look contradictory for the time consumption it takes initially for the projects, but in long term Refactoring is extremely rewarding.

Being vigilant to look for flaws in our own work and improving them makes us better engineers. A better practice with refactoring will improve the developer’s first inhibition of coding.

Efficient Code 

It is vital that engineers write efficient code. The primary objective is to make programs execute faster with the minimum storage, taking the least memory for execution. This skill is crucial for the fact that, depending upon projects, there will be different criteria that cannot be compromised. When there is resource limitation or the projects rely heavily on minimum time such as real-time dependent projects, engineers need to deliver providing the most efficient code possible. There would be differences in performance in the order of magnitudes while performing with large size of data, and the ability to write efficient code comes in handy in these situations.


Documentation refers to the process of proper illustration of the code such that every complex part of the code is described properly to other developers to understand and follow up within the code to the degree that other new developers and engineers can modify the code as per their need by reading the documentation alone. Documentation makes it really easy to understand the code, its various components, and navigate around the system. Just like how the Booklet of any device such as a Washing Machine helps us understand how to use the machine, the documentation of the code for a project will help the engineers know how to go about the code.

Documentation can be supported to a project in various ways such as adding comments like Inline Comments which are line level comments in the program itself. The Docstrings will help understand the functions and modules of a program and Project Documentation describes and defines the Project as a whole.

Thus, in this article, we learned about various common practices adopted in the software industry for production-level codes. A brief details of what production code was discussed moving into the ways to maintain the production quality code through the process of writing clean code, modularization, refactoring, writing efficient code, and documentation.