AI-assisted coding aims to help you as a developer be more productive, write code faster, make fewer mistakes, and switch between windows and your IDE less frequently. Is AI-assisted coding, however, a solution, a scam, or anything in between?
We’ll look at the advantages and disadvantages of adopting AI-assisted coding in this article.
What is AI-Assisted Coding and How Does It Work?
A machine learning model that has been trained on previous code is used to power AI-assisted coding. The most advanced of these models have been trained on billions of lines of code from reputable open-source projects all across the world. The model “learns” what characters and lines of code are frequently followed by one another as a result of this training. It then gives auto-suggest tab completions for you while you type your code, right in your IDE.
As an extremely simple example, if you type import React in your IDE, the autocomplete would provide something like from ‘react’; to finish your statement.
However, AI-assisted coding takes a step further by learning from the code you write. The model is continuously looking at how you write code and what patterns you like to use. You may even train a more advanced model on your team’s code repositories to help it better grasp how your organisation produces code, allowing you to be more consistent as a group.
What Are the Benefits?
So, why would you choose to code with an AI-assisted assistant? Perhaps you’d prefer to rely on your own judgement and a less sophisticated auto-suggest option.
To begin with, AI-assisted coding keeps you in your IDE, minimising the amount of time you spend navigating between windows. If the autocomplete can supply you with the correct syntax, you won’t need to do a fast Google search to remember how to use an API you’re not familiar with. Higher productivity comes from fewer context switches.
Because the code that these machine learning models are trained on generally follows best practices and well-known design patterns, they can also prompt you to write better code. These nudges can be used as a teaching tool to help you become a better developer.
What Are the Potential Downsides?
Why would you want to avoid using AI-assisted coding?
The most serious worry is privacy. If the machine learning model is trained not only on public code but also on code you write, it’s possible that your code will be shared with others who utilise the same machine learning model. As a result, before utilising an AI-assisted coding solution, always read the product’s privacy statement to see if and how the product gathers or shares data.
The model’s training is the second point of concern. As previously stated, the majority of models are trained on billions of lines of code. But what if those code repositories aren’t trustworthy? “Garbage in, garbage out,” as the phrase goes. If the model was trained on bad code, the suggestions you get will be just as bad.
Top 5 coding assistants
1) Tabnine
With AI completions, you can code faster. Tabnine (previously Codota) is the world’s most popular AI code completion tool, with over 1 million developers using it to complete code in a variety of programming languages.
With Tabnine’s AI autocompletions, you can cut your coding time in half, decrease errors, and learn best practises. Tabnine’s strong Artificial Intelligence assistant integrates seamlessly with your IDE, allowing you to code without interruption.
Tabnine AI uses deep learning to analyse publicly available code in order to forecast and recommend code completions that save time. Your code belongs to you and no one else.
Tabnine’s local completion model runs on your machine and doesn’t transfer any of your code anywhere, allowing you to work even while you’re not connected to the internet. How will you put your improved coding speed to use? Getting twice as much done or finishing in half the time? The AI-powered autocompletion in Tabnine eliminates the need to input entire lines of code, memorise syntax, or worry about typos.
2) Jet Brains Datalore
It is a strong environment for Jupyter notebooks . In Jupyter notebooks, use smart coding assistance for Python, run code on powerful CPUs and GPUs, communicate in real time with your team, and easily share the results. With a pre-setup environment and clever code editor, you can start working in seconds.
With powerful CPU and GPU processors, you can upload datasets to persistent storage, evaluate data, and train machine learning models. Send a link to your work and ask your team to collaborate. Beginner data scientists and analysts will benefit from basic devices with minimal functionality. For advanced data scientists and analysts, powerful devices with limitless functionality are available.
For enterprises and teams who demand further protection and customization, on-premises installation and professional support are available. Start for free online or install for your team on-premises. Create secret variables to store credentials and upload files and folders to cloud persistent storage.
3) Kite
Kite helps in faster coding and Stays in the zone. Kite enhances your code editor with AI-powered code completions, giving devs superpowers. To add Kite’s AI-powered code completions to all of your code editors, download the Kite engine.
Kite has over 16 languages and 16 code editors to choose from. You’ll get lightning-fast completions that are aware of your code’s context. Give your code editor superpowers and receive lengthier multi-line completions where you might not otherwise get any. Stay in the groove by coding faster.
Kite’s AI can help you reduce keystrokes by up to 47% in this case. With a single click or mouse-over, you may access Python documentation, as well as examples and how-tos. Find files in your codebase that are connected to the current file you’re working on quickly making thousands of developers more productive.
4) IntelliCode
IntelliCode helps you save time by prioritising the tasks you’re most likely to complete. The IntelliCode suggestions are based on thousands of open source projects with over 100 stars on GitHub. The completion list is tailored to promote common practises when combined with the context of your code.
IntelliCode’s capabilities go beyond statement completion. Signature assistance also suggests the most likely overload for your situation.
IntelliCode can make recommendations based on your code and communicate them with your team in real time. You can use this preview feature to create a team model for code that isn’t in the open source domain, such as methods on your own utility classes or domain-specific library calls. Integrate our build job into your pipeline to ensure that your team’s completions are kept up to date when the repository changes.
5) OpenAl Codex
Codex can currently interpret simple commands in natural language and execute them on the user’s behalf, allowing for the creation of a natural language interface to existing programmes.
Codex is proficient in more than a dozen programming languages. Through our API, we are now enabling businesses and developers to build on top of OpenAI Codex.
OpenAI Codex is a successor of GPT-3, and its training data includes natural language as well as billions of lines of source code from publicly accessible sources, such as GitHub repositories. OpenAI Codex is most adept in Python, but it also knows JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell. It has 14KB of RAM for Python code, compared to only 4KB for GPT-3, allowing it to take into consideration over 3x as much contextual data when doing any activity.
To know more about Al assisted programming options contact info@keleno.com
