AutoDevMate – AI Assistant for Pull Request Reviews & Boilerplate Code Generation
Software development teams often face two key productivity bottlenecks:
- Repetitive manual tasks like generating boilerplate code and project scaffolding
- The burden of reviewing pull requests (PRs) thoroughly and consistently
Reviewing PRs is time-consuming, error-prone, and often suffers from human oversight or delay. Similarly, setting up project code from scratch is tedious and limits time for innovation. These issues collectively reduce developer velocity and satisfaction.
AutoDevMate is an AI-powered assistant that streamlines software development by automatically reviewing PRs and generating clean, functional code scaffolds — using IBM Granite large language models at its core.
Developers interact with AutoDevMate through:
- A GitHub-integrated PR review bot: Intelligently reviews code diffs, summarizes changes, and offers line-specific, human-like suggestions to improve clarity, correctness, and efficiency.
- A CLI or web assistant: Instantly generates boilerplate code (e.g., REST APIs, React components, test suites) from natural language prompts.
- An interactive chat interface, enabling developers to ask:
- “What does this PR change?”
- “Refactor this function to make it more readable.”
- “Create a Dockerfile for this app.”
This reduces boring, repetitive tasks, improves code quality, and frees developers to focus on higher-level design and problem-solving.
- Software engineers across all experience levels
- Engineering managers aiming to improve code review workflows
- DevOps and QA engineers automating validation tasks
AutoDevMate is more than a code generator or linter — it mimics the critical thinking of experienced engineers. It provides contextual, nuanced feedback and builds entire project structures from vague ideas. Unlike rigid rule-based tools, it adapts to natural developer language and coding conventions.
By leveraging IBM Granite, AutoDevMate understands and generates enterprise-grade code, enabling a new level of AI assistance in the software lifecycle. It shortens review cycles, speeds up onboarding, and reduces burnout from menial dev work.
AutoDevMate integrates IBM Granite large language models (LLMs) across three core components:
Granite is used to:
- Summarize PR changes in plain language
- Analyze diffs and provide optimization or security suggestions
- Detect missing test cases and poor patterns by reasoning over logic and structure
Granite is prompted with full context (e.g., entire functions or classes) to provide actionable feedback, like a peer code reviewer.
Granite LLMs generate complete code scaffolds from simple prompts such as:
- “Create a Flask API with login and MongoDB integration”
- “Generate a React login component with validation and styling”
It handles naming, folder structures, multi-file output, and stylistic consistency with minimal user input.
This chat interface enables real-time developer support using Granite:
- Explain code, recommend refactors, or generate unit tests
- Create Dockerfiles, CI/CD configs, or pipeline templates
- Suggest alternate implementations or improve snippets
IBM Granite models provide enterprise-grade reliability, contextual reasoning, and advanced natural language + code synthesis. Their robustness, accuracy, and performance across complex dev workflows make them ideal for real-world engineering use.
AutoDevMate demonstrates Granite’s potential to eliminate friction, save time, and transform mundane tasks into intelligent, automated workflows.
- Create python virtual environment.
- Run pip install requirements.txt to install requirements.
- on gitbash setup API keys as follows-
- export IBM_WATSONX_API_KEY=<Your_API_KEY>
- export IBM_WATSONX_PROJECT_ID=<Your_Project_key>
- export IBM_WATSONX_URL=https://us-south.ml.cloud.ibm.com
- Run application using following command-
- streamlit run autodevmate.py