Tessera2025/Tessera2025

📚 Tessera: Exposing the Challenges of LLM-based Test Generation for Low-Resource Programming Languages

Tessera is a validation benchmark designed to measure how well models can generate unit tests for Low-Resource Programming Languages (LRPLs) — specifically Rust, Go, and Julia.

📌 Purpose

Evaluate how well a model can generate test code, given a focal function's source code and additional context.

📂 Dataset Structure

Each sample contains:

• —function_name: Name of the focal function.
• —focal_code: Raw source code of the focal function (used for context).
• —function_component: Detail information about the function like function signature,arguments definition,line range,...
• —file_content: Content of file have the focal function.
• —file_path: Relative path to the file in the repository. Additional fields like package_name, wrap_class, class_signature, or struct_class may depending on the programming language. If the language does not use these concepts, the values will be None or empty.

Dataset Size

The dataset contains ~372–412 samples per language, depending on the source repository.

Usage

from datasets import load_dataset
# Load full dataset
dataset = load_dataset("solis-soict/Tessera")
# Load individual language splits
rust_dataset = load_dataset("solis-soict/Tessera", split="rust")
go_dataset = load_dataset("solis-soict/Tessera", split="go")
julia_dataset = load_dataset("solis-soict/Tessera", split="julia")

Additional Information

Other Resources:

• —Github:
• —Paper:

Licence Information

MIT License

Citation Information