Comparing Artificial Intelligence Generated Code in Programming Languages

Artificial Intelligence

In the age of limitless possibilities, the widespread use and re-inventing accessibility of generative Artificial Intelligence solutions provide a dazzling array of creativity for programmers. These uses include creating artwork and writing codes. The growth of software has outpaced all trends in consumer technology over the past ten years. How we work is changing rapidly, and the realm of software development is the same.

For many developers, using AI programming tools is now a standard procedure. Find out and compare the best AI software generators for code that are the best for creating excellent code with artificial intelligence.

Do you want to know more? Learn more!

Can You Program Using AI Software?

It is indeed possible to code using AI tools. In reality, using AI tools for programming is feasible and can significantly improve efficiency and precision.

AI Code is created under the name AI or artificial intelligence (AI). AI programs can develop their programs or even translate an existing programming language into another. They can also assist in tasks like providing assistance with auto-generating documentation and searching for the smallest code fragments quickly.

The most well-known tool is OpenAI’s Codex, an AI system that converts natural language into code. Codex is the engine behind GitHub Copilot, another popular AI software for writing code.

OpenAI Codex can understand basic commands using natural language and perform them for the programmer. This allows developers to extend the application with an interface for natural language.

As a general-purpose programming model, OpenAI Codex can be used for almost every programming task. However, the tool is still in the beta stage, so outcomes will differ.

AlphaCode, is a different tool that has been shaking up the field. It is interesting to note that this tool performs better than human coders in certain scenarios. AlphaCode outperformed 45% of participants in competitions for coders that had at least 5,000 participants.

Code generators also have issues. This is why AI programming software is used to assist developers in becoming more efficient and productive rather than replacing the tools altogether. For instance, AI Development Company and software engineers are likely to trigger security holes in their apps. In addition, copyright concerns remain unanswered.

Also, AI code tools are not completely secure. However, their growing popularity suggests that they should not be ignored.

How Does Generative AI Code Generation Work?

Generative AI for coding is a possibility due to recent advances in large-language model (LLM) technologies and natural language processing (NLP). It uses deep learning algorithms and massive neural networks based on massive databases of diverse source code. The training code is typically derived from code that is publicly accessible and developed through open-source projects.

Programmers use plain text messages describing what they want the code to accomplish. Generative AI tools can suggest codes or complete functions that streamline the coding process by taking care of repetitive tasks and reducing the need for manual code. Generative AI can also translate code from one language to another, facilitating the conversion of code or modernization projects like updating old applications through converting COBOL into Java.

While code created by AI, generative AI, and LLM technology improve its accuracy, it is not without imperfections and needs to be reviewed and edited by humans. Specific software that uses generative AI for software tools automatically generates unit tests that aid in this.

Different Types Of AI-Generated Code in Various Programming Languages

GitHub Copilot

Copilot was developed in collaboration with GitHub and OpenAI. It is an AI-powered tool for code completion that seamlessly integrates into the most popular Integrated Development Environments (IDEs), such as Visual Studio Code, offering contextually aware code suggestions and completions when you write.

It uses OpenAI’s Codex Language Model, which is developed from various code repositories to provide code suggestions while developers write. OpenAI Codex is most powerful in Python but can also work with other languages, including JavaScript, Go, Perl, PHP, Ruby, and TypeScript.


TabNine is an AI code assistant that can learn from the codebase it is working on. It provides real-time code completion, chat, and code generation. It also includes syntax formatting, language recognition, and documentation. TabNine is compatible with Java, Python, JavaScript, SQL, and other popular languages. It is also integrated with code editors such as VSCode, IntelliJ, and PyCharm.

Experience unbeatable security, privacy, and compliance when you host Tabnine’s AI assistant in your desired environment, whether on-premises or in a virtual cloud (VPC).

Tabnine doesn’t train your code unless you link your source code. If you connect your codebase with Tabnine, the code you write will never leave your system and remain private. Additionally, it comes with Strict policy management. Apply the configuration policy for Tabnine’s AI assistant throughout your company.

OpenAI Codex

Today’s most famous AI programming tool is OpenAI Codex, a powerful AI code generator. It was taught using huge code based on its LLMs (GPT-3, GPT-3, and GPT-4) of OpenAI, which naturally incorporates the capability to comprehend and write code. OpenAI Codex will allow you to code software in over 12 languages, including Go, TypeScript, Perl, PHP, BASH, Ruby, JavaScript, and Swift. The algorithm was built using trillions of lines of accessible code on sites like GitHub repositories, which makes it a flexible and powerful tool for programmers and developers.

OpenAI is a world-class AI Research and Development business with products used by millions of users worldwide. OpenAI Codex is an AI model built on the GPT-3 family of large language models, like the OpenAI ChatGPT chatbot. It translates natural language into code and has an easy user interface. 

This has been developed using thousands of codes. It is most effective in Python and Python, but it also supports over 12 coding languages. OpenAI Codex is currently available in a private beta, and in the initial time, it will be available for free.

Google AI Code Generation Tools

Google has several AI software programs for code generation, each with its particular strengths and areas of focus. Google’s Gemini (formerly Bard), an LLM, is a computer that has been trained using an extensive database of code and text. It can create images, text, and code. It is compatible with various programming languages, including C++, Go, Java, JavaScript, Python, and TypeScript.

Google Cloud’s Vertex AI leverages Pathways Language Model 2 (PaLM 2) to create codes and text in response to prompts from conversations. In addition, Duet AI for Developers is an AI copilot powered by Google models. It is available within IDEs (like VS Code or PyCharm) and provides live assistance with coding, similar to the GitHub Copilot.

Amazon CodeWhisperer

Amazon CodeWhisperer generates code suggestions, including snippets and complete functions, in real-time within the IDE using your feedback and the code you have. It also supports CLI completions and natural-language-to-bash translation in the command line.

Amazon CodeWhisperer generates code suggestions, including snippets and full functions, in real-time in the IDE, basing its suggestions on your inputs and existing code. It also supports CLI completions and natural-language-to-bash translation in the command line. CodeWhisperer allows you to mark or filter code suggestions that look like publicly accessible code. Find the open-source repository’s URL and license to review the code and add the attribution quickly.

Examine your code for difficult-to-find security flaws and receive suggestions for code to fix the identified vulnerabilities. AI-powered suggestions for code are adapted to your specific application’s code so you can swiftly accept fixes with confidence and focus on more valuable work.

CodeWhisperer is an excellent choice for how you work. Choose one of 15 programming languages, including Python, Java, and JavaScript, and your most preferred developed environments (IDEs), which include VS Code, IntelliJ IDEA, Visual Studio (preview), AWS Cloud9, the AWS Lambda console, JupyterLab, and Amazon SageMaker Studio. Your preferred CLIs, which include macOS Terminal, iTerm2, and the built-in VS Code terminal, are also available.


Polycoder is among the first open-source AI-powered code generators that concentrate on creating code for specific programming tasks. PolyCode is a powerful AI code generator available for download on GitHub. It employs complex algorithms to handle natural language processing and code creation. The available datasets on GitHub are larger than 200GB. Furthermore, the models are constantly improved with the help of high-end hardware.

The program provides many pre-trained models for 12 different programming languages, such as PHP, C, JavaScript, Python, C++, Ruby, TypeScript, and many more. The program can write C more efficiently than another type of model, including Codex. Anyone can improve it by instructing PolyCoder in different programming languages, supplying it with data from other code repositories, or increasing its natural language comprehension.


CodeT5 is a revolutionary model that utilizes Transformer technology to help you comprehend and generate code. It is based on T5 architecture, a neural language model with a sequence-to-sequence (Seq2Seq) structure. CodeT5 expands the denoising Seq2Seq purpose of T5 by introducing two identifier tags and forecast tasks that allow the model to better use the token type information derived from programming languages. 

This is essential since programming languages employ identifiers, terms, or symbols given by programmers to help identify various elements of code, like variables, functions, and classes. CodeT5 is a program that uses the objective of bimodal dual learning to allow bidirectional conversion between programming and natural language. This method assists with natural language programming alignment by helping the model comprehend the link between natural language and code. 

This technique trains a model on massive amounts of data before being refined on specific programming tasks to increase accuracy and efficiency.

Code Llama

Coding Llama by Meta, an open-source AI model based on Llama 2, was specifically designed to generate and discuss code. Compared to other publicly accessible LLMs, it excels at programming tasks.

Code Llama’s goal is to streamline developer processes, facilitate novice coders’ learning, and increase the robustness of software and documentation. By focusing on openness to AI development, the release encourages innovation and the development of secure, responsible AI tools.

Smol Developer

Smol is an open-source artificial intelligence system designed to serve as an individual junior developer capable of creating a complete codebase based on your product specifications. Instead of traditional, rigid start-up template designs, Smol could make virtually any software based on your specific requirements. Boasting a simple, safe, and small codebase offers the perfect blend of ease of understanding, customization, and a helpful, harmless, and honest approach to AI development.

AI Coding Tools Features

This section will discuss the most commonly used capabilities of AI programming tools and explore the many ways it can help AI development companies to improve the development process. In addition to the features described here, some AI programming tools are adept at refactoring code and documentation, giving explanations, and searching for specific files and code fragments.

Coding Completion

AI code completion suggests code snippets of code as developers type by anticipating the code that will be written based on the context. This decreases the need for manual input, improves code consistency, and reduces mistakes, which is particularly beneficial when performing repetitive tasks.

Generation Of Code

AI code generation is a procedure that transforms natural language prompts to describe the desired function into executable codes. It takes in the suggestions and provides pertinent code, considering the established programming patterns.


Code translation refers to converting your source code to a desired programming language. It enables you to seamlessly integrate code from various teams within your company or other open-source projects. It can also help reduce the cost and effort of updating outdated codebases written in old programming languages such as COBOL and Ada.

Review Of code

Code review is a grading procedure that uses AI’s potential to improve the quality of code by identifying and correcting errors. AI software for coding may create explanations and comments written in natural English, making it easier to comprehend the review process. This is especially beneficial to developers who want to get precise feedback to improve their coding abilities.

Furthermore, AI tools can evaluate code for readability and consistency to ensure that it adheres to established style guidelines and is read and understood by others who are developing it.

Problems With AI Code Generation

However, adopting AI Code Generation currently comes with its issues. It raises concerns regarding the quality and reliability of code generated by ChatGPT and outlines the potential dangers associated with the widespread use of ChatGPT. Like any technology that is still in its infancy, there are concerns about issues such as:

Reliability And Quality Of Code

How can AI-generated code be trustworthy? Studies have demonstrated that, while typically reliable, AI assistants can sometimes create insecure or unstable code, which highlights the necessity of thorough code review. The study that assessed the accuracy of code in Copilot-generated code showed that of the 164 issues that were tested, 47 (28.7 percent) were correctly solved, the remaining 84 (51.2 percent) were partly correct, and 33 (20.1 percent) were wrong.


In a bid to meet the developer’s requirements or fulfill them, Generative AI may produce software that is more complicated than necessary. This can result in unnecessary abstractions, a lot of code layering, or complex logic. Complex and complicated code structures could challenge maintenance, debugging, and collaboration.

The Risk Of Technical Debt

Generative AI models may be able to prioritize the generation of syntactically accurate codes but only sometimes optimize them for efficiency, quality, or maintainability. This can result in codes that are difficult to comprehend and modify or increase technical debt.

Do You Want To Dive Into The AI Code Generation Pool?

The answer is optimistic. For developers and businesses planning to use the tools available, taking a planned approach is crucial. Here are some guidelines to ensure seamless and efficient integration

Start small by playing around with AI assistants for specific tasks or in coding languages. This will allow you to evaluate their efficacy and discover the potential issues before implementing them throughout your process.

Prioritize the quality of your code. Do not unquestioningly trust AI-generated code. Utilize rigorous testing and validation processes to ensure the code is reliable, bug-free, and safe.

Improve your workforce’s skills. Train your employees on the advantages and weaknesses of AI assistants. Instruct them to utilize AI tools as aids rather than replacements to enhance their own skills. You can also consider taking help from AI consulting firms

Final Take

Now you know what AI tools can do to assist you in working more efficiently by making development more accessible. But even though AI tools for programming will help developers work more efficiently, it is first necessary to select the best developers to join your team.


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