AI-Assisted Programming Using Open Source Models

  • Mon, Jan 8, 2024
  • 7-minute read

Table of Contents

What is AI Assisted Programming

AI-assisted programming or coding is an emerging trend in software development. This technology leverages artificial intelligence to aid software developers in various aspects of coding, from writing and reviewing code to debugging and optimizing it. AI in programming not only enhances productivity but also democratizes software development, making it more accessible to a broader range of people.

Features of AI-Assisted Programming

  • Code Suggestions and Generation - AI-assisted programming tools automatically suggest or generate code, speeding up the development process. It efficiently creates contextually appropriate boilerplate code and snippets allowing developers to focus on more complex tasks.

  • Automated Test Generation and Documentation - AI-assisted programming streamlines the creation of tests and documentation, ensuring code reliability and maintainability while saving significant developer time.

  • Error Detection and Correction - AI algorithms can detect errors, ranging from syntax issues to more complex logical errors, and suggest corrections. This feature significantly reduces debugging time.

  • Automated Code Refactoring - AI can suggest optimal ways to refactor code, ensuring it is clean, maintainable, and efficient.

  • Natural Language Processing (NLP) - AI programming tools understand natural language queries, allowing developers to write code using plain English, which is especially beneficial for novice programmers.

  • Data Analysis and Prediction - AI tools can analyze large datasets to predict outcomes, optimize performance, and automate repetitive tasks within the coding process.

Benefits of AI-Assisted Programming

  • Increased Productivity - Automation of repetitive tasks and intelligent code suggestions significantly speed up the development process.

  • Enhanced Code Quality - Consistent detection and correction of errors lead to cleaner, more efficient code.

  • Learning and Development - Beginners can learn coding more effectively with interactive, AI-powered tools that offer guidance and corrections.

  • Inclusivity and Accessibility - With AI tools that understand natural language, programming becomes more accessible to non-experts, breaking down barriers to entry in the tech world.

Commercial AI-Assisted Programming Tools

  • GitHub Copilot - A collaboration between GitHub and OpenAI, Copilot offers AI-powered code suggestions directly in the coding environment.

  • JetBrains IDEs - JetBrains offer AI-driven coding assistance with it’s suite of IDEs.

  • Cursor - Cursor is a popular AI-driven coding IDE built on VSCode.

Open Source AI-Assisted Programming Models & Tools

Open source large language models present several advantages as alternatives to commercial models and tools. Key benefits include:

  • Data Privacy - For many organizations, controlling their data is paramount. Utilizing open-source models can allow them to manage data internally without the risk of external parties accessing it.

  • Customization and Flexibility - These models offer flexibility for developers to train them using specific datasets. This can include applying filters on certain topics, and tailoring the model more closely to the organization’s unique needs.

  • Cost-Effectiveness - Open source software is generally free to use, modify, and distribute. This accessibility makes it a cost-effective solution for individuals, businesses, and organizations, particularly those looking to minimize expenses without compromising on quality and flexibility.

  • Collaboration and Community Development - Open source projects thrive on community contributions. They bring together diverse perspectives and skill sets, leading to more robust, innovative solutions. This collaborative spirit not only accelerates software development but also enhances its quality through continuous peer review and contributions.

mindmap root("AI Assisted Programming") ::icon(fa fa-human fa-robot) ("Models") ::icon(fa fa-cubes) ("Code Llama") ("WizCoder") ("Etc.") ("Local LLM Stack") ::icon(fa fa-computer) ("Ollama") ("Llamafile") ("Etc.") ("IDE Extensions") ::icon(fa fa-code) ("VSCode") ("CodeGPT") ("Continue") ("Jet Brains IDEs") ("CodeGPT") ("Continue")

Models

Here are some popular open source models:

  • Code Llama - Open-sourced by the Meta Engineering team, the Code Llama is a family of large language models for code based on Llama 2 providing state-of-the-art performance. It supports many of the most popular programming languages used today, including Python, C++, Java, PHP, Typescript (Javascript), C#, Bash and more.

  • Wizard Coder - Wizard Coder is another popular code generation model based on Code Llama.

  • Starcoder - StarCoder is a code generation model trained on 80+ programming languages.

Info
You can find the various open source models supporting code generation in Hugging Face library. Further, you can refer a benchmark of various code generation models here.

Running models locally

Here are some zero-friction tools and IDE extensions to run open source model locally.

  • Ollama - Ollama enables you to run a large language models on a local computer or self-hosted on Docker/Kubernetes. You can pull a model from Ollama library and get started instantly. For guidance on installing and running Ollama on your local machine, please refer to the details in my previous post Ollama - Running Large Language Models on Your Machine.
  • Lamafile - Lamafile is another great project from Mozilla Foundation to run models locally. You can find more about Lamafile here.

IDE Extensions

Next, it’s essential to integrate IDE extensions that link with models operating either locally or on your network. These extensions provide an experience almost like the commercial versions of coding assistants, enhancing your coding process with features like code generation and suggestions, automated test creation, documentation assistance, and support in debugging or resolving issues.

Here are some free IDE extensions that you can use with open source models.

  • CodeGPT - CodeGPT is a free IDE extension for AI-assisted programming available for both Visual Studio Code and JetBrains IDEs. You can find instructions to set up CodeGPT with Ollama here.

  • Continue - Continue is another great free IDE extension for AI-assisted programming available for both Visual Studio Code and JetBrains IDEs. You can find the model set up guide here.

Warning
The output from open source models could be under the purview of third-party licenses, including open source licenses. It’s recommended to examine the relevant license and terms of use as needed.

Building Custom Models

Building custom models based on a foundational base model, particularly using an organization’s specific codebase involves fine-tuning a pre-trained large language model (LLM) with the unique datasets derived from an organization’s own code repositories. The objective is to leverage the powerful capabilities of the base LLM - which has already been trained on vast, diverse data - and tailor it to align closely with the specific coding patterns, practices, and requirements unique to the organization.

Such an approach offers a dual advantage. Firstly, it allows organizations to harness the sophisticated AI capabilities of the base model, such as understanding complex language structures or generating code. Secondly, and more importantly, it imbues the model with a deep understanding of the organization’s specific coding environment and knowledge base. This customization leads to a more relevant and efficient AI assistant, capable of providing more relevant code suggestions, error detection, learning assistance to new joiners and other programming aids that are finely tuned to the organization’s unique technology landscape.

This results in a powerful, bespoke AI assistant that enhances coding efficiency, reduces error rates, and overall, accelerates the software development lifecycle within the organization.

Here is a high level overview of the process to build bespoke models.

flowchart TD A[Private Code Repositories] -->|Extract Code Data| B[Data Preparation] B -->|Processed Data| C[Custom Training of Base LLM] C -->|Train on Code Data| D[Trained LLM for Programming] D -->|Evaluate & Fine-Tune| E[Model Evaluation and Optimization] E -->|Finalize Model| F[Deployment of Customized LLM] F -->|Integration| G[IDE Extensions for AI-Assisted Programming] G -->|Enhanced Coding Experience| H[End User: Programmers] style A fill:#f9f,stroke:#333,stroke-width:4px style B fill:#bbf,stroke:#333,stroke-width:4px style C fill:#fbf,stroke:#333,stroke-width:4px style D fill:#bfb,stroke:#333,stroke-width:4px style E fill:#ff9,stroke:#333,stroke-width:4px style F fill:#fbb,stroke:#333,stroke-width:4px style G fill:#bbf,stroke:#333,stroke-width:4px style H fill:#9ff,stroke:#333,stroke-width:4px

Ending

AI-assisted programming is truly revolutionizing the way we write and interact with code. Its impact extends beyond mere productivity; it’s reshaping the landscape of software development, making it more efficient, inclusive, and accessible to a broader audience. As AI technologies continue to evolve, we can expect even more innovative and transformative tools to emerge in this space.

Happy AI-assisted programming!

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