Artificial Intelligence Tools that help to generate code

In this article, we will discuss top Artificial Intelligence Tools that help to generate code to help new programmers.

AI-generated code is becoming increasingly accurate, and it is only a matter of time before AI completely replaces human programmers. AI-generated code is not perfect, but it is getting better constantly. Some AI tools are even able to generate code that is more accurate than human-written code. This is a huge step forward, and it shows that AI can be used to create better code faster.

Artificial Intelligence Tools that help to generate code

There are many Artificial Intelligence tools available to programmers but we have some of the most popular AI tools:

1. Polycoder

The Polycoder is based on OpenAI’s GPT-2, which was trained using a 249 GB codebase developed in 12 different programming languages. The creators of PolyCoder claim that the software can write C more precisely than any other model, including Codex. Polycoder is one of the earliest open-source code-generating models, even if most code generators are not.

Pros: Polycoder can generate code from natural language prompts or code snippets. It can also handle multiple languages and domains. It can outperform other models like Codex and GPT-Neo in some languages, such as C, JavaScript, Rust, Scala, and TypeScript.

Cons: Polycoder can be complex to install and use, requiring technical knowledge and dependencies. It can also generate incorrect or incomplete code that may need manual debugging or editing.

2. Cogram

Cogram creates code for data scientists and Python programmers using Jupyter Notebooks and SQL queries. English-language queries may be written by data scientists and converted by the tool into sophisticated SQL queries with joins and grouping. It works with MySQL, SQLite, PostgreSQL, and Amazon Redshift.

Cogram is integrated with Jupyter Notebooks by Python and Julia developers to automatically produce code. The tool may provide contextual code for a particular job based on the remarks. Even common Python modules like Matplotlib, Plotly, or Seaborn may be used by data scientists to create visualizations.

Pros: Cogram can generate code from natural language prompts or code snippets. It can also generate comments, tests, and documentation for the code. It can handle various tasks, such as web development, data analysis, and automation.

Cons: Cogram requires an invitation to access. It can also generate incorrect or incomplete code that may need manual debugging or editing.

3. DeepCode

DeepCode is a code review tool powered by AI that examines your code and makes suggestions for improving it. Code completion, refactoring, and lining are among its many capabilities. For open-source projects, DeepCode is free, while a premium membership is available for private enterprises.

Pros: DeepCode can analyze code in real time and provide suggestions for improvement. It can also integrate with various IDEs, version control systems, and code review platforms. It can support multiple languages, such as Java, Python, JavaScript, TypeScript, C#, and Go.

Cons: DeepCode requires a subscription to access. It can also have false positives or negatives that may need manual verification or correction.

4. CodeWP

CodeWP is designed and optimized for those who construct WordPress websites. CodeWP offers JavaScript and PHP support and settings to plugins like WooCommerce and major page builders. It is simple to write queries, shortcodes, and finish activities on time.

Pros: CodeWP can generate code snippets from natural language prompts or keywords. It can also provide explanations and references for the code. It can handle various WordPress tasks, such as creating custom post types, taxonomies, widgets, menus, and shortcodes.

Cons: CodeWP requires a license to access. It can also generate incorrect or incomplete code that may need manual debugging or editing.

5. Visual Studio IntelliCode

Microsoft Visual Studio IntelliCode is a product that allows AI-assisted coding. It is compatible with Java, Python, JavaScript, and TypeScript in Visual Studio code and also supports C# and XAML in Visual Studio. This AI code completion tool was trained on the codes of 500,000 open-source projects on GitHub that have at least 100 stars. As a result, it can provide you with wiser recommendations while considering the context and present code.

Pros: VS IntelliCode can provide intelligent code completion, refactoring, formatting, testing, and debugging. It can also learn from the user’s code style and preferences. It can support multiple languages, such as C#, C++, Java, Python, TypeScript, JavaScript, SQL, and XAML.

Cons: VS IntelliCode is a proprietary tool that requires a subscription to access some features. It can also have false positives or negatives that may need manual verification or correction.

6. PyCharm

PyCharm is an integrated program that is specially designed for Python programmers. This is used to create the most complex software. It allows you to develop AI code completion capability and complete coding tasks faster. It also enables rapid repairs, error checks while moving, smooth project navigation, and intelligent code completion.

Pros: PyCharm can support various Python frameworks, such as Django, Flask, Pyramid, and web2py. It can also integrate with various tools and platforms, such as Git, GitHub, Docker, Kubernetes, and Jupyter Notebook. It can provide intelligent code completion, syntax highlighting, code formatting, and error detection.

Cons: PyCharm can be slow and resource-intensive, especially when working with large projects or files. It can also have compatibility issues with some Python libraries or packages. It can be expensive for professional or enterprise users

7. Ponicode

To create software free of errors, you should use Ponicode, which will save you time. You can develop code more easily and quickly. You can also create industrial-grade software free of bugs and errors. This is free to use with Java, Python, JavaScript, and TypeScript programming languages.

Pros: Ponicode can generate code from natural language prompts or keywords. It can also generate tests and documentation for the code. It can handle various tasks, such as web development, data analysis, and automation. It can support multiple languages, such as Python, JavaScript, TypeScript, PHP, Java, and C#2.

Cons: Ponicode requires a subscription to access. It can also generate incorrect or incomplete code that may need manual debugging or editing. It can have false positives or negatives that may need manual verification or correction

8. AIXcoder

It employs deep learning models trained using millions of lines of open-source code. As a result, it also provides principles that suites your situation. For Data security and privacy, you can also use the offline version so that you can save the code on your computer. It compiles coding statistics to provide you with custom code inspection criteria.

Pros: AIXcoder can provide intelligent code completion, refactoring, formatting, testing, and debugging. It can also learn from the user’s code style and preferences. It can support multiple languages, such as Java, Python, C++, C#, JavaScript, TypeScript, SQL, HTML, CSS, and XML3.

Cons: AIXcoderrequires a subscription to access. It can also have false positives or negatives that may need manual verification or correction. It can have compatibility issues with some IDEs or platforms

9. CodeT5

Researchers at SalesForce created the open-source programming language paradigm known as CodeT5. The T5 (Text-to-Text Transfer Transformer) framework from Google is its foundation. The researchers used approximately 8.35 million instances of code, together with user comments, from openly available GitHub projects to train CodeT5. The bulk of these datasets was obtained from the CodeSearchNet dataset, containing two C and C# datasets from BigQuery, along with Ruby, JavaScript, Go, Python, PHP, and C and C#.

Pros: CodeT5 can generate code from natural language prompts or code snippets. It can also handle multiple languages and domains. It can outperform other models like Codex and GPT-Neo in some languages.

Cons: CodeT5 can be complex to install and use, requiring technical knowledge and dependencies. It can also generate incorrect or incomplete code that may need manual debugging or editing.

10. OpenAI Codex

GitHub Copilot, a tool from GitHub to produce code inside common development environments such as Neovim, VS Code, JetBrains, and even in the cloud with GitHub Codespaces, is powered by OpenAI Codex, a model based on GPT-3. It claims it can write code in at least 12 different languages, including BASH, JavaScript, Go, Perl, PHP, Ruby, Swift, and TypeScript. The algorithm is trained on trillions of lines of publicly accessible code from places like GitHub repositories.

Pros: OpenAI Codex can generate code from natural language prompts or code snippets. It can also generate comments, tests, and documentation for the code. It can handle various tasks and languages.

Cons: OpenAI Codex requires an invitation to access. It can also generate incorrect or incomplete code that may need manual debugging or editing. It can pose security risks or ethical concerns.

That's It!