Embedding-based search in JetBrains IDEs
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Abstract (arXiv)
Most modern Integrated Development Environments (IDEs) and code editors have a feature to search across available functionality and items in an open project. In JetBrains IDEs, this feature is called Search Everywhere: it allows users to search for files, actions, classes, symbols, settings, and anything from VCS history from a single entry point. However, it works with the candidates obtained by algorithms that don't account for semantics, e.g., synonyms, complex word permutations, part of the speech modifications, and typos. In this work, we describe the machine learning approach we implemented to improve the discoverability of search items. We also share the obstacles encountered during this process and how we overcame them.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 26 Jan 2024.
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7 of 15 references.
References · 7
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,107
- 2020Longformer: The Long-Document Transformercited by 3,393
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,435
- 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,229
- 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 857
- 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 651
- 2021CoSQA: 20,000+ Web Queries for Code Search and Question Answeringcited by 75
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