https://github.com/mohammad-malik/cpp_dryrun_codegen
A tool for generating and testing "dry runs" (tricky C++ code snippets often asked in exams by universities like FAST) in an isolated Docker environment.
https://github.com/mohammad-malik/cpp_dryrun_codegen
cpp dryr exam help llms rag
Last synced: about 2 months ago
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A tool for generating and testing "dry runs" (tricky C++ code snippets often asked in exams by universities like FAST) in an isolated Docker environment.
- Host: GitHub
- URL: https://github.com/mohammad-malik/cpp_dryrun_codegen
- Owner: mohammad-malik
- Created: 2025-01-27T18:25:20.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T07:12:01.000Z (about 2 months ago)
- Last Synced: 2025-04-05T08:20:44.770Z (about 2 months ago)
- Topics: cpp, dryr, exam, help, llms, rag
- Language: Python
- Homepage:
- Size: 78.1 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Agentic C++ Dry Run Code Generator and Runner
A tool for generating and testing "dry runs" (tricky C++ code snippets often asked in exams by universities like FAST) in an isolated Docker environment.
## Overview
This project provides a system for:
- Generating challenging C++ code snippets that test understanding of tricky language concepts taking inspiration from various PF and OOP past papers through Retrieval Augmented Generation (RAG).
- Running the code snippets safely in a virtual execution environment using Docker, to ensure accuracy.
- Automated testing and verification (as the outputs are fed back to a reasoning LLM, which generates until it has 10 various questions that meet the criteria and are worth studying).
- A web interface to view the newly generated code snippets, their outputs, and an explanation for the output.
## Features
- Focuses on tricky C++ concepts like:
- sizeof() and datatype size edge cases
- Order of operations (e.g., x++ + x++)
- Conditional statement edge cases
- Loop patterns
- Pointer manipulation and const correctness
among many others asked by FAST in courses like Programming Fundamentals (PF) and Object Oriented Programming (OOP).
## Setup
1. Prerequisites:
- Python 3.x
- Docker
- Required Python packages (see requirements)
2. Environment Variables:
- Generate an API Key from [Google AI Studio](https://makersuite.google.com/app/apikey)
- Create a `.env` file with:
```
GOOGLE_API_KEY=your_api_key_here
```
## Usage
### With python virtual env:
1. Download `install.sh`
2. Set it as executable: `chmod +x install.sh`
3. Wait.
4. When done, set `run.sh` as executable: `chmod +x ./run.sh`
5. Run it. `./run.sh`
### Alternatively, for those with conda:
1. Download `install_condabased.sh`
2. Follow the steps above.