# Query Expansion Based on Crowd Knowledge for Code Search

arXiv: https://arxiv.org/abs/1703.01443 · HTML: https://mlsanity.com/paper/1703.01443/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2017.

## Built on by (most cited first, 18 of 18)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search](https://mlsanity.com/paper/2201.10866/) (2022)
- [Backdooring Neural Code Search](https://mlsanity.com/paper/2305.17506/) (2023)
- [Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search](https://mlsanity.com/paper/2401.04514/) (2024)
- [Code Search based on Context-aware Code Translation](https://mlsanity.com/paper/2202.08029/) (2022)
- [Rethinking Negative Pairs in Code Search](https://mlsanity.com/paper/2310.08069/) (2023)
- [Exploring Representation-Level Augmentation for Code Search](https://mlsanity.com/paper/2210.12285/) (2022)
- [Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection](https://mlsanity.com/paper/2202.12481/) (2022)
- [Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow](https://mlsanity.com/paper/2102.00826/) (2021)
- [CodeScout: Contextual Problem Statement Enhancement for Software Agents](https://mlsanity.com/paper/2603.05744/) (2026)
- [BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?](https://mlsanity.com/paper/2104.08017/) (2021)
- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)
- [OASIS: Order-Augmented Strategy for Improved Code Search](https://mlsanity.com/paper/2503.08161/) (2025)
- [AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees](https://mlsanity.com/paper/2201.07984/) (2022)
- [Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering](https://mlsanity.com/paper/2604.22759/) (2026)
- [XSearch: Explainable Code Search via Concept-to-Code Alignment](https://mlsanity.com/paper/2605.16046/) (2026)

## Abstract (arXiv)

As code search is a frequent developer activity in software development practices, improving the performance of code search is a critical task. In the text retrieval based search techniques employed in the code search, the term mismatch problem is a critical language issue for retrieval effectiveness. By reformulating the queries, query expansion provides effective ways to solve the term mismatch problem. In this paper, we propose Query Expansion based on Crowd Knowledge (QECK), a novel technique to improve the performance of code search algorithms. QECK identifies software-specific expansion words from the high quality pseudo relevance feedback question and answer pairs on Stack Overflow to automatically generate the expansion queries. Furthermore, we incorporate QECK in the classic Rocchio's model, and propose QECK based code search method QECKRocchio. We conduct three experiments to evaluate our QECK technique and investigate QECKRocchio in a large-scale corpus containing real-world code snippets and a question and answer pair collection. The results show that QECK improves the performance of three code search algorithms by up to 64 percent in Precision, and 35 percent in NDCG. Meanwhile, compared with the state-of-the-art query expansion method, the improvement of QECK Rocchio is 22 percent in Precision, and 16 percent in NDCG.