UERR: A Unified Effective Retrieval Model for Open-Source Repositories

Open-source repositories are widely used to improve the productivity and quality of modern software development. However, it is generally not an easy task for developers to find suitable repositories from a large-scale platform, e.g., GitHub. Although GitHub provides a search engine to support repository exploration, the search capability is limited as some important criteria (e.g., the closed ratio of issues) are not supported, and users cannot specify fine-grained preferences on different criteria. To address these limitations, we propose a unified effective repository retrieval model, UERR. We first define a unified schema useful for retrieving repositories by analyzing the metadata of GitHub repositories. Our schema includes 30 attributes grouped into six dimensions, such as functionality, maintenance, and popularity. Based on the schema, we design a query grammar for users to express their fine-grained requirements with personalized preferences and an integrated method to measure the relevance between a repository and a query. In particular, we expand acronyms and split conjoined words by leveraging large language model (LLM) APIs and design a weighted N-gram based method to measure functional relevance between textual descriptions of repositories and queries. Experiments with 20 diverse queries show that UERR significantly improves the top 1~20 retrieved repositories by 27.12%~165.87% in terms of the Pre@k, MRR@k, and NDCG@k metrics, in comparison with the search engines provided by Elasticsearch and GitHub. The results also validate the superiority of our weighted N-gram based functional relevance measurement method over two representative methods, i.e., the IDF-weighted keyword matching and word embedding matching.