StarCoder - amosproj/amos2025ss04-ai-driven-testing GitHub Wiki

StarCoder is a permissively licensed large language model developed by Hugging Face and ServiceNow under the BigCode initiative. It is designed specifically for code generation, completion, and understanding, and is optimized for open-source, on-premise deployment.

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🔍 Overview

StarCoder is a transformer-based language model trained primarily on a large corpus of permissively licensed source code and related documentation. It supports numerous programming languages and is particularly well-suited for tasks involving software engineering, including automatic test generation, code explanation, and bug localization.

The model is distributed under the OpenRAIL-M license, which allows for research and commercial use with minimal restrictions.

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🔧 Key Features

Code-Centric Design

Specifically trained on code from GitHub repositories using permissive licenses (MIT, Apache 2.0, BSD).

Can handle multiple languages including Python, JavaScript, C++, Java, Go, Bash, and more.

Input/Output Flexibility

Accepts prompts via CLI, API, or scripting interfaces.

Works well with structured prompts for generating unit tests or documentation.

Model Sizes

Available in multiple sizes:

StarCoderBase (15.5B) – full model

StarCoder2 (3B, 7B, 15B) – lightweight versions for efficient local inference

Large Context Window

Supports up to 8192 tokens of context, which allows it to process entire functions, classes, or small files in one prompt.

Instruction Tuning Support

Compatible with fine-tuning via SFT (Supervised Fine-Tuning) and LoRA techniques.

Open Source

Available via Hugging Face and integrated with Ollama, enabling local deployment in Docker or bare-metal environments.

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🧠 Architecture

Based on decoder-only transformer architecture (similar to GPT-2/GPT-3)

Uses multi-head self-attention

Custom tokenization optimized for code (e.g., newline-aware, indentation-aware)

Trained on 1+ trillion tokens of permissively licensed code

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🎯 Relevance for Our Project

StarCoder is particularly well-suited for our goals of automatic test code generation and local on-premise use:

Local Execution: Runs easily on local machines via Ollama (ollama run starcoder) — no cloud dependency.

Open Source: Licensed under OpenRAIL-M, which permits modification and commercial use.

Python-Compatible: Easy to integrate with Python scripts for file-based input/output workflows.

Instruction-Following: Understands and responds well to test-generation prompts (e.g., "write unit tests for this function").

Code Specialization: Trained on real-world codebases and supports multiple languages (Python, C++, JS, etc.).

Efficient Deployment: Lightweight versions (3B and 7B) are suitable for laptops without needing GPU servers.

These capabilities make StarCoder a practical and powerful choice for integrating AI into our test automation pipeline, meeting both technical and licensing constraints of the project.

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Practical Benefits:

Produces usable unit test stubs with correctly structured pytest/unittest output

Can extend existing test code

Easy to integrate in your pipeline using a simple Python wrapper

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📖 References

Hugging Face – StarCoder Model Card: 🔗 https://huggingface.co/bigcode/starcoder

BigCode Project GitHub (official repo): 🔗 https://github.com/bigcode-project

Ollama Model Page for StarCoder: 🔗 https://ollama.ai/library/starcoder

BigCode StarCoder Documentation & Technical Report: 🔗 https://huggingface.co/docs/bigcode/starcoder

OpenRAIL License (via BigCode License Overview): 🔗 https://huggingface.co/spaces/bigcode/license