Open Source LLM/AI Programming Plugin Based on JetBrains IDE

Introduction

In simple terms, AutoDev is positioned as a private large language model + highly integrated AI programming assistant. AutoDev offers an AutoCRUD model, which involves the following design understanding process:

1. Gather requirements from the requirement management system and conduct requirement analysis.
2. Combine the source code with the requirement system to select the most suitable entry point for changes (such as Controller in Java).
3. Analyze the requirements and Controller with AI to achieve code generation.
4. Gradually auto-complete other parts of the code based on the Controller (in progress…)

Another mode is the standard Copilot mode, which can integrate with existing large model tools to achieve a series of AI code assistance functions.

By integrating LLM, we can not only generate code but also generate unit test code, thereby improving testing efficiency and coverage.

Let’s take a deeper look at the new possibilities based on existing AI capabilities.

Requirements: Automation Convergence, Analysis, and Improvement

In the current scenarios, there have been a series of attempts to integrate AI into requirement management:

• Automation Improvement. Automatically identify and supplement missing requirement information from user feedback and data analysis, such as automatically identifying user-raised issues and converting them into requirement descriptions, auto-completing keywords and tags for requirements, etc.
• Automation Analysis. By training with built-in domain knowledge, better evaluation and optimization of requirements can be achieved, discovering potential problems and opportunities, thus improving the efficiency and effectiveness of requirements.
• Automation Convergence. By combining other AI technologies, such as intelligent recommendations, dialogue systems, and multi-party collaboration, you can better communicate and coordinate requirements, collect and integrate user feedback and pain points, improving satisfaction and consistency of requirements.
• Automation Iteration. By combining AI data with human feedback, requirements generation can be better updated and improved, adapting to the constantly changing environment and user needs, thus enhancing the sustainability and innovation of requirements.

Despite the existing solutions such as LangChain and llama-index only temporarily supporting OpenAI, it will be easier to implement in the future with the addition of more open-source large language models.

Toolchain: Intelligent IDE

For the current scenarios, there is already a wealth of tools available, such as:

• Automated Code Review
• Automated Testing
• Automated Log Analysis
• AI-Assisted Programming
• ……

Indeed, for paid AI tools like GitHub Copilot, the cost may be secondary for most companies; the primary concern is the security of the code. While various new models are emerging domestically, most lack integration related to programming or have weaker programming capabilities. However, there are models specifically for programming, such as the 16B CodeGen model provided by Salesforce on Hugging Face. Although some minor tuning is still required, as Replit has stated, the results are quite good.

Subsequently, there is the encapsulation of large language models like AutoDev, simplifying the development process for ordinary developers.

Documentation: Beyond Search

With LLM and various intelligent Q&A capabilities, we can also incorporate internal documentation and code of various tools to provide a more comprehensive and intelligent documentation service. For example, Q&A documentation built with LangChain can perform semantic understanding and intelligent Q&A on various internal documents of the enterprise, thereby simplifying the learning cost for developers.

If AutoDevAI is an auxiliary programming tool, it likely combines artificial intelligence technologies to help developers improve programming efficiency and code quality. Here are some possible application scenarios:

Code Completion and Repair:

• While writing code, AutoDevAI can provide code completion suggestions, reducing typing.

• Automatically identify and fix errors or potential security vulnerabilities in the code.

Code Generation:

• Based on the developer’s description or comments, AutoDevAI can generate complete functions or classes.

• Automatically generate repetitive code, such as configuration files, database migration scripts, etc.

Code Optimization:

• Provide suggestions for code refactoring, helping to improve code performance and readability.

• Automatically optimize resource usage, such as memory management and CPU utilization.

Intelligent Search:

• Quickly search relevant documentation, API guides, or code examples to help developers find solutions to problems.

• Search for specific code snippets or functionality implementations in the codebase.

Code Review:

• Before code submission, AutoDevAI can perform automated code reviews to ensure code compliance.

• Analyze code style and potential code smells, providing improvement suggestions.

Testing Assistance:

• Automatically generate test cases, assisting developers in unit testing and integration testing.

• Analyze test coverage and suggest additional tests.

Project Management and Planning:

• Provide project planning and task allocation suggestions based on project needs and resources.

• Predict project progress and potential risk points.

Education and Training:

• Assist in programming education by providing instant feedback and guidance to students.

• Help novice developers understand and master programming concepts more quickly.

Cross-Language Development:

• Support multiple programming languages, helping developers maintain efficiency when switching between different languages.

Collaboration Enhancement:

• In team collaboration, AutoDevAI can help unify code styles and standards.

• Analyze team members’ working patterns and suggest ways to improve collaboration efficiency.

It is important to note that the specific application scenarios of AutoDevAI may vary based on its functional characteristics and design purposes. If AutoDevAI is a specific product or service, its application scenarios may be described in more detail in its official documentation or marketing materials.

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