Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Intelligent Change Impact Analyzer

Project Overview

A graph-based system for analyzing the impact of code changes in software repositories.
It extracts structural information from the code, builds a dependency graph, and identifies components affected by a given change.

The system integrates machine learning to classify and rank impacted components, helping developers focus testing efforts and reduce regression risks.

Features

Fetch commits from a GitHub repository
Select and compare two commits
Analyze code structure using AST
Build dependency graph of functions/modules
Predict impact severity using ML

Setup

Clone the repository
Create a virtual environment:
```
python -m venv venv
```

Activate it:

Linux/macOS:
```
source venv/bin/activate
```
Windows:
```
venv\Scripts\activate
```

Install dependencies:
```
pip install -r requirements.txt
```

Create a .env file:

FLASK_DEBUG=1MONGO_URI=<MongoDB connection string>GITHUB_TOKEN=<GitHub personal access token>HF_TOKEN=<HuggingFace access token>GEMINI_API_KEY=<Gemini API key>GEMINI_MODEL=gemini-2.5-flash-lite

Optional:

# Disable all ML tagging entirely.DISABLE_ML_TAGGER=1# Or disable only the local model path.# With USE_LOCAL_MODEL=0, the app can still use Hugging Face Spaces.# If the Space is unavailable, it will not fall back to the local model.DISABLE_ML_TAGGER=local_only# Defaults to local model inference.# Set to 0/false to use the Hugging Face Spaces Gradio client instead.USE_LOCAL_MODEL=1# Optional when USE_LOCAL_MODEL=0HF_SPACE_ID=VantaTree/MLCodeTagger

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

python ml_tagger/build_raw_dataset.py
python ml_tagger/dataset_builder.py

2. Configure training (one-time)

accelerate config

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

This machine
No distributed training
Do you want to run on CPU only? → NO
torch dynamo → NO
DeepSpeed → NO
GPU ids → 0
NUMA efficiency → NO
Mixed precision → fp16

If you do NOT have a GPU

This machine
No distributed training
Run on CPU only → YES

3. Train model

python ml_tagger/train.py

Model and dataset are stored locally and are ignored by git.

Run Application

python app.py

Open: http://127.0.0.1:5000

Notes

ml_tagger/data/ and model/ are excluded from git
Dataset is generated locally for reproducibility
Only the final trained model is required for inference

AI Summary

The analysis page can now generate an optional AI summary on top of the deterministic graph analysis.

How it works:

services/analyzer.py builds the normal impact result
services/ai_summary.py converts that result into a compact LLM prompt
If GEMINI_API_KEY is configured, the app requests a structured summary from the Gemini API
The summary is rendered in the AI Summary section of the analysis page

If AI config is missing or the API call fails, the rest of the analysis still works normally.

Name		Name	Last commit message	Last commit date
.vscode		.vscode
ml_tagger		ml_tagger
modules		modules
services		services
static		static
templates		templates
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
app.py		app.py
db.py		db.py
megadump.json		megadump.json
requirements.txt		requirements.txt
runtime.txt		runtime.txt

Name		Name	Last commit message	Last commit date
.vscode		.vscode
ml_tagger		ml_tagger
modules		modules
services		services
static		static
templates		templates
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
app.py		app.py
db.py		db.py
megadump.json		megadump.json
requirements.txt		requirements.txt
runtime.txt		runtime.txt

Name		Name	Last commit message	Last commit date
.vscode		.vscode
ml_tagger		ml_tagger
modules		modules
services		services
static		static
templates		templates
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
app.py		app.py
db.py		db.py
megadump.json		megadump.json
requirements.txt		requirements.txt
runtime.txt		runtime.txt

Name		Name	Last commit message	Last commit date
.vscode		.vscode
ml_tagger		ml_tagger
modules		modules
services		services
static		static
templates		templates
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
app.py		app.py
db.py		db.py
megadump.json		megadump.json
requirements.txt		requirements.txt
runtime.txt		runtime.txt

Name		Name	Last commit message	Last commit date
.vscode		.vscode
ml_tagger		ml_tagger
modules		modules
services		services
static		static
templates		templates
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
app.py		app.py
db.py		db.py
megadump.json		megadump.json
requirements.txt		requirements.txt
runtime.txt		runtime.txt

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Intelligent Change Impact Analyzer

Project Overview

Features

Setup

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

2. Configure training (one-time)

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

If you do NOT have a GPU

3. Train model

Run Application

Notes

AI Summary

About

Resources

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Watchers

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Releases

Packages

Contributors

Languages

Latest commit

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Folders and files

Repository files navigation

Intelligent Change Impact Analyzer

Project Overview

Features

Setup

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

2. Configure training (one-time)

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

If you do NOT have a GPU

3. Train model

Run Application

Notes

AI Summary

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Intelligent Change Impact Analyzer

Project Overview

Features

Setup

ML Setup (Dataset + Training)

1. Build dataset (local only, not stored in repo)

2. Configure training (one-time)

Recommended settings:

If you have an NVIDIA GPU (RTX / GTX)

If you do NOT have a GPU

3. Train model

Run Application

Notes

AI Summary

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors