Skip to main content

The best practices that should adopt a computer programmer

Programming best practices - Programming - Computer programming


Programmers employ a variety of techniques to improve the quality of their software development efforts. To preserve the quality of the code base, certain programmers, always write legible and clean code. Meanwhile, some programmers employ outstanding optimization techniques to build efficient and user-friendly source codes. This is a list of excellent practices that any developer can use to improve their program. As a result, the majority of experienced engineers document these programming methods in order to provide guidance to the entire software development team.

Programming best practices are a set of activities that most programmers accept and follow - but they are not rules, therefore anyone can reject them when designing software. As a result, time-consuming workloads will have to bear the brunt of the effects. Consider what happened to the bug you neglected the last time. On modern, fast-moving codebases, forgetting a defect will dramatically increase the time it takes to fix it.

As a result, keeping a checklist of best practices in mind is a terrific idea for producing high-quality software and having fun with programming. I've been programming for over a decade and have experience in a variety of fields including desktop application development, mobile app development, and web development.


Based on my many learnings during my software development experience, I've compiled the following list of programming techniques. These best practices apply to every software development project you work on on a regular basis:

1st practice: Use the best architecture and technologies available.

Software architecture models describe how programmers might arrange and create a software system's core structure: Because late architectural modifications are time-consuming, choose the most appropriate core architecture and implementation architecture the first time around.

Also, no matter what the current trend is, choose the correct technology for your purposes; the current trend doesn't matter if it doesn't address your problem.

Practice 2: Focus on the core before moving on to the features.

Adding features to the guts of unstable software systems necessitates lengthy rewrites of the software. As a result, it's crucial to build a stable system core before adding features, because the core's stability determines how long your software system lasts.

For improved stability and maintenance, most software development teams divide off core and feature modules. Apple, for example, maintains Darwin, an open-source operating system that adds functionality to device-specific operating systems such as iOS and macOS. Meanwhile, the Golang team rarely modifies the core language and frequently expands the standard library with new features.

Practice 3: Produce high-quality code

With a reasonable, simple, and clear code structure, good code achieves the anticipated effects. Although there is no such thing as perfect code, there is such a thing as decent code. Use the most appropriate data structures and design patterns to write effective code with a well-defined code style guide. During the development phase, refactor your code to increasing manageability factors.

However, don't strive to make your code ideal by incorporating unwelcome design patterns, rigorous coding style guides, or complicated data structures. Even if you create a flawless codebase in your opinion, other team members will disagree. Each developer's idea of perfect code is different, yet all developers admire good code.




Practice 4: Only add a code snippet if you fully comprehend it.

Code snippets are commonly obtained from internet forums, tutorials, and open-source projects by programmers. Other developers, like us, wrote these public code snippets. As a result, these code snippets may contain flaws and incompatibilities.

As a result, inspecting each code snippet carefully before copying and pasting it into your software projects is always a smart idea. Check for APIs that are no longer supported, features that are incompatible, code style flaws, and performance issues.

Also, always completely comprehend what your code modification accomplishes; this will allow you to adjust the codebase without adding new bugs, software quality concerns, or security flaws.

Practice 5:  Never hide bugs, prevent them and fix them.

Although software errors are unavoidable, we can detect them sooner and make the required corrections. We can avoid software issues by detecting them early in the development or testing process. Most problems can be avoided while developing an algorithm by carefully examining all possible outliers.

With today's culture of rapid software development, early detection of flaws is more difficult. Furthermore, improving every piece of code until it receives the bug-free certificate is impractical. As a result, after the first development phase, practically all software development teams prioritize bug repairs.

Coding reviews typically concentrate on code styles, design patterns, and language syntax. As a result, we can easily conceal problems in the code. Hidden bugs, on the other hand, develop slowly and require more time and work to correct later. As a result, never hide bugs; instead, prevent and fix them to save time in the future.



The best Python code editors and IDEs for Windows, Linux, and Mac


Programming best practices relate to a set of commonly accepted behaviors that any programmer can use to create high-quality software. They are not formal rules or standards. In this tale, we explored some generic programming methods that you may apply to any software project. Based on their development experiences, any developer can create their own best practices. As a result, combine your programming knowledge with the aforementioned list to construct a unique collection of best practices to use in your everyday programming work.
















Comments

Most Popular

What are the advantages for a programmer to use Python in Machine Learning?

  Python in Machine learning With its astonishing qualities, Machine Learning (ML) is fast altering the world of technology. Making appointments, checking the calendar, playing music, and displaying programmatic adverts are all examples of how machine learning is slowly infiltrating our daily lives. The technology is so precise that it anticipates our demands even before we are aware of them. Machine learning offers a lot of potential and has a bright future. Learning machine learning with Python programming, on the other hand, has its own set of advantages. The intricacy of the scientific discipline of machine learning might be intimidating, so it's crucial to focus on the most critical things first. A machine learning expert should have a thorough understanding of its algorithms, which will hopefully make their journey easier. Object identification, summarization, prediction, classification, clustering, re...

Python in Data Science and Machine Learning

  Python in Data Science - Python Libraries Python's popularity in the data science industry has exploded in recent years, and it's now the programming language of choice for data scientists and machine learning professionals trying to improve the functionality of their apps. Python also includes a huge number of libraries that help data scientists execute complex jobs without having to deal with a lot of code. Python is one of the world's third most popular programming languages. We'll go through 7 Python libraries that can assist you in creating your first data science application in the sections below. Numpy In many data science initiatives, Arrays are the most significant data type. NumPy is a software library that provides a wide range of multidimensional array and matrix operations and is us...

What skills must you master in order to be a good data scientist?

  Data science - Data - Data scientist - Skills - Cloud - 5G - Technical report Why the cloud has become an opportunity for a data scientist? What is good practice for writing a relevant technical report? The goal of data science is to make the most of data. This is when data management enters the picture. Data management is the process of transforming data from one form to another. This is critical since data science entails creating models, testing new features, and performing deep dives. There's no doubting that data science is all about maximizing the value of raw data. Simply described, it is the process of extracting useful information from large amounts of unstructured data. There is no better way to organize and analyze data than to use statistics. Statistics aid in the identification of correlations between data sets. Analytical concepts play a big role in data science. The success of a firm is directly linked to the qualit...

What is Social Media Analytics?

Social media analytics - Social media analytics tools - Business intelligence Social media analytics is the process of extracting business insights from social media platforms such as Facebook, Twitter, and Instagram. Likes and shares aren't the only metrics used in social media analytics. Even counting the number of answers, comments, and link hits are insufficient. This approach also helps organizations to measure client sentiment and discover trends as a subfield of social media marketing. In a nutshell, it entails using social media to track the effectiveness of activities taken as a result of these decisions. The concept of social listening is also included in Social Media Analytics. Listening entails keeping an eye on social media for issues and possibilities. Listening is generally integrated into more comprehensive reports that include listening and performance analysis in social media analytics solutions. It uses software tools to convert modulated and non-modulated data i...

The best Python code editors and IDEs for Windows, Linux, and Mac

  IDEs for Windows, Linux, and Mac An integrated development environment (IDE) is a software tool that gives computer programmers a lot of power when it comes to developing software. A source code editor, build automation tools, and a debugger are the most common components of an IDE. Intelligent code completion is available in most current IDEs. - IDEs allow programmers to unify the various parts of building a computer program and boost programmer productivity by adding features like source code editing, executable creation, and debugging. - IDEs are familiar with your language's syntax and can provide visual clues and simpler-to-read keywords by graphically clarifying the syntax. They're also usually quite effective at anticipating what you'll enter next, making coding considerably faster and easier. - Integrated development environments (IDEs) handle reading Python code, running Python scri...