#codeefficient

[centizen]

Maximizing Code Reuse: Strategies for Reusability Across Projects

Code reuse is a widely adopted technique in the tech industry to achieve high efficiency, particularly when using open source frameworks. The concept of code reuse can be applied in two ways:

Reusing code that you have written

Reusing external code for your own projects.

To meet the increasing demand for smarter products and software, it is crucial to ensure that the code you reuse is safe and of high quality and that the libraries you reuse are secure and trustworthy.

The importance of code reusability

Code reuse is a vital practice in the tech industry, particularly in projects that use open source frameworks. There are two main approaches to code reuse: reusing code that has already been written and reusing external code for your own project. By reusing code, developers can save significant time that would have been spent on rewriting functionality from scratch. For example, a developer who spends weeks writing code for socket communication can reuse the same code in another project, leading to faster project completion times. Many organizations maintain shared databases of code components, with up to 80% of code blocks already built and shared among team members. Consistent code reuse provides scalability, gives developers full control over development, and reduces the risk of failure. Ultimately, code reuse leads to faster development times, improved consistency, and reduced risk of failure, making it a crucial practice in modern software development environments.

Classification of code reuse

To achieve faster development with fewer resources, there are various classifications of code reuse that organizations can implement:

Internal: Reusing an organization’s own components internally, which may involve copying existing code into new projects. Code redundancy must be carefully managed in this type of reuse.

External: Integrating third-party licensed components to reduce development costs. Teams must carefully analyze available options to ensure the best fit for product requirements.

Planned: Strategically designing building blocks of code to meet the future needs of the organization.

Opportunistic: Analyzing project requirements and determining which existing components can be reused in the initial stages of a project.

Referenced: Focusing on the form and structure of code, with reused code having a reference and a specific version.

Ways to reuse a code

There are several critical factors to consider when writing reusable code. However, these attributes alone are not enough unless they serve a comprehensive strategy. Here are some ways to make code reusable:

Modularization

Dividing a software system into independent chunks that perform a specific function increases manageability and eliminates the need to repeatedly write code.

Cohesion

The way that two modules of a class or system cooperate to establish a sense of purpose. Cohesive code is reusable across multiple projects and can adapt to different environments.

Loosely coupled software modules

Code that is portable and unaffected by external linking. Developers need to manage a few links between functions and classes to strike a balance, as zero coupling is not a good option.

Utility classes

Creating modern utility classes can be used in multiple projects. Useful methods such as data formatting and sharing, validation constraints, image sharing, compression and image download, etc., can be added to these classes.

Flexibility

Ensuring flexibility in code allows it to be used in future projects with minor alterations and serve the required purpose without crashing.

DRY principle

Don’t write lines of code that are not needed and follow the DRY principle of “Don’t repeat yourself.”

One module performs one thing at a time

Each module should perform one thing at a time. For example, a method for file compression should only take a file as input and return the compressed form as output. This technique decouples the method from the rest of the project for efficient reuse.

Code reuse methodologies

There are numerous ways to reuse code without having to rewrite or copy/paste from existing sources. Below are a few common methodologies:

Inheritance

Inheritance is widely used in object-oriented programming (OOP) paradigms. Developers can easily reuse function overriding to make various methods work differently for multiple objects.

Libraries

Dynamic linked libraries (DLL) are another concept to consider. DLLs have implications in .NET applications, and third-party libraries designed to perform specific functions facilitate code reuse.

Forking

Forking is a basic process in Git. It involves using someone else’s code by altering it to fit your own needs and distributing it further under your own tag. This exercise is useful for open-source projects, such as making modifications under the hood and creating a new framework.

Methods/Function Calls

With function calls, changes are made in one place and then reflected everywhere else where that function is called. This enables you to change the code just once to meet a user’s request.

There are many other methodologies to use under different paradigms, but the above are the most basic ones to give you an idea of the varying degrees of code reuse.

Code reuse in practice

Code reuse leads to high-quality end products developed in a shorter time. Large enterprises should document every third-party component and address vulnerabilities before use. Best methods for dealing with repetitive tasks include Git Submodules, RAD Stack, creating shared libraries, Monorepos, and Bit.

Git submodules

Git submodules create a subdirectory for another Git repository for shared code. It can be used in multiple projects but creates coupling between projects and requires manual updates.

RAD stack

RAD stack (React Native and Django) supports all platforms and reuses common frontend components. Its open-source framework provides a vast collection of libraries, cross-site scripting, and scalable APIs. Add or remove services to build fast and future-proof apps.

Creating your own libraries

Shared libraries hold the code to be reused in a warehouse image, but create redundancy across multiple projects and require complicated dependency management.

Monorepos

A single repository for multiple packages allows for keeping components together. Lerna helps to configure, unify, and publish multiple components but requires extensive refactoring and management of dependency graphs.

Bit

Bit controls unified code changes and component dependency trees for smooth code reuse. It defines and tracks shared components, displays their dependency graphs, and enables testing in an isolated environment. Changes can be synchronized for other projects by updating the version of the component.

Summary

Reusable code is crucial in modern app development. To make it easy and collaborative, create code as reusable segments, share them within your development community, and distribute them as open source for adoption and collaboration.

[vyechi]

Generic Repository Pattern in C#

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Optimizing your Python Code for Beginners

Optimizing your Python code can help make it run faster and use less memory. Here are some ways you can optimize your code to make it faster and easier to read:

Profile your code: Before you start optimizing, you need to know where the bottlenecks are in your code. You can use the built-in cProfile module to profile your code and identify which parts are taking the most time.

Use built-in functions and libraries: Python has a lot of built-in functions and libraries that are highly optimized. For example, the math library has highly optimized functions for performing mathematical operations. By using these built-in functions, you can often achieve significant performance gains.

Use NumPy and SciPy: NumPy and SciPy are powerful libraries for working with large arrays and matrices of data. They are highly optimized and can be much faster than using built-in Python data structures like lists and dictionaries.

Avoid using global variables: Global variables are variables that are defined at the top level of a module and can be accessed from anywhere within that module. They can make your code harder to understand and debug, and they can also slow down your code. Instead, try to use local variables and pass them as arguments to functions.

Use list comprehensions and generator expressions: List comprehensions and generator expressions are powerful tools for working with lists and sequences of data. They can be much faster than using for loops, and they also make your code more readable.

Avoid unnecessary computations: Make sure your code is only performing computations that are actually necessary. For example, if you're iterating over a list of data and you only need to keep track of the maximum value, you don't need to keep track of all the other values as well.

Use the right data structures: Python has several different data structures to choose from, like lists, dictionaries, and sets. Each data structure has its own strengths and weaknesses, so make sure you're using the right one for the job.

Avoid using recursion: Recursive functions can be hard to understand and debug, and they can also slow down your code. Instead, try to use loops or other iterative structures.

Use caching: Caching is a technique that allows you to store the results of expensive computations so you can reuse them later without having to recompute them. This can help speed up your code and reduce memory usage.

Use compiled extensions: If you have a performance-critical section of code that you can't optimize any further, you can consider writing it in a lower-level language like C or C++ and then using a Python extension module to call it from your Python code. You can also use specific techniques to optimize different types of code, such as optimizing code for parallel processing, or for specific types of data. It is also important to keep in mind that premature optimization can make code harder to read and maintain. It is always a good idea to start with writing readable and maintainable code, and then optimize only where necessary.

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[centizen]

Java vs Python – Which programming language is good?

For a few decades now, Java vs Python both have been around and so has the debate of which is better. We are all entitled to our opinions and I am here to help you make your decisions by pointing out the pros and cons of both the languages.

Similar to many other programming languages these are commonly used to create basic applications or customised software products.

Let us take a side by side comparison of both the languages.

The all-rounder

Java, although takes a little more effort to learn, runs on any OS platform without any indifference. Java is popularly known as WORA (Write Once Run Anywhere). However, both Java and Python do compile as bytecode. The main difference is that Java is a compiling language whereas Python is an interpreted language that executes codes by line.

Its all in the CODE!

Beginners have always found Python as a smoother teacher. It appears as an intuitive coding style that has simpler syntaxes in comparison to Java.

Python uses whitespaces to mark the beginning and end of the blocks of code. Java, however, uses braces for the same purpose.

Python comparatively uses few lines of code as it does not include braces, and uses white space to mark the start & closings of a module.

The deal with Variables.

Java does not allow the data type of a variable to be changed, while Python does. Unlike traditional programming languages, Python allows you to assign a string to a variable that once held an integer value. This is not possible with the statically typed language Java, where you have to declare the variable names and their type in an explicit manner. On the contrary, Python is looked upon as a dynamically typed language that doesn’t stress this.

Unlike the traditional programming languages, Python lets you assign the new value to a variable that previously stored a different value. This is not possible with Java, and you need to define a variable for each corresponding type.

On the other hand, Python is simpler & more forgiving than Java even in simpler cases as such reusing old variables.

The Flash Test — Speed & Portability

JComparing both languages, their speed and performance varies. Java is notably faster than Python. However, it cannot handle CPU intensive tasks, as it executes an entire class and not by lines. But with the choices at their disposal, they can replace Python’s runtime with CPython, PyPy, Cython. Java does not need additional tools for optimisation.

Python has tons of modules and a quick learning progress that allows you to manipulate it so that it could do almost anything for you added it being an open source language.

In terms of Java, being platform independent it lets you run your code on any machine that supports JVM. And because of its popularity, the JVM is already installed in most systems.

Make a choice !

Albeit, novices choose Python as you can see what the outcome will be immediately. Professionals prefer Java as it is used for complex applications and software development invariably across platforms.

If you are interested in becoming a developer or an architect or data science engineer then Java fits you. In case you are interested in the role of modern developers and fields such as Robotics and AI then you should master Python.

In conclusion we are telling you, do not limit yourself to one language.