ArkTS-CodeSearch: A Open-Source ArkTS Dataset for Code Retrieval

TL;DR

This paper introduces the first large-scale ArkTS dataset and benchmark for code retrieval, enabling improved code understanding and retrieval tasks within the OpenHarmony ecosystem.

Contribution

It provides a comprehensive ArkTS dataset from open-source repositories and evaluates models for ArkTS code retrieval, filling a critical gap in ArkTS code intelligence research.

Findings

High-performing model for ArkTS code understanding achieved

Effective cross-platform deduplication of ArkTS functions

Established the first systematic benchmark for ArkTS code retrieval

Abstract

ArkTS is a core programming language in the OpenHarmony ecosystem, yet research on ArkTS code intelligence is hindered by the lack of public datasets and evaluation benchmarks. This paper presents a large-scale ArkTS dataset constructed from open-source repositories, targeting code retrieval and code evaluation tasks. We design a single-search task, where natural language comments are used to retrieve corresponding ArkTS functions. ArkTS repositories are crawled from GitHub and Gitee, and comment-function pairs are extracted using tree-sitter-arkts, followed by cross-platform deduplication and statistical analysis of ArkTS function types. We further evaluate existing open-source code embedding models on the single-search task and perform fine-tuning using both ArkTS and TypeScript training datasets, resulting in a high-performing model for ArkTS code understanding. This work establishes the first systematic benchmark for ArkTS code retrieval. Both the dataset and our fine-tuned model are available at https://huggingface.co/hreyulog/embedinggemma_arkts and https://huggingface.co/datasets/hreyulog/arkts-code-docstring .