Revisiting Code Search in a Two-Stage Paradigm

• URL: http://arxiv.org/abs/2208.11274v3
• Date: Thu, 28 Mar 2024 03:51:21 GMT
• Title: Revisiting Code Search in a Two-Stage Paradigm
• Authors: Fan Hu, Yanlin Wang, Lun Du, Xirong Li, Hongyu Zhang, Shi Han, Dongmei Zhang,
• Abstract summary: TOSS is a two-stage fusion code search framework. It first uses IR-based and bi-encoder models to efficiently recall a small number of top-k code candidates. It then uses fine-grained cross-encoders for finer ranking.
• Score: 67.02322603435628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With a good code search engine, developers can reuse existing code snippets and accelerate software development process. Current code search methods can be divided into two categories: traditional information retrieval (IR) based and deep learning (DL) based approaches. DL-based approaches include the cross-encoder paradigm and the bi-encoder paradigm. However, both approaches have certain limitations. The inference of IR-based and bi-encoder models are fast, however, they are not accurate enough; while cross-encoder models can achieve higher search accuracy but consume more time. In this work, we propose TOSS, a two-stage fusion code search framework that can combine the advantages of different code search methods. TOSS first uses IR-based and bi-encoder models to efficiently recall a small number of top-k code candidates, and then uses fine-grained cross-encoders for finer ranking. Furthermore, we conduct extensive experiments on different code candidate volumes and multiple programming languages to verify the effectiveness of TOSS. We also compare TOSS with six data fusion methods. Experimental results show that TOSS is not only efficient, but also achieves state-of-the-art accuracy with an overall mean reciprocal ranking (MRR) score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713. Our source code and experimental data are available at: https://github.com/fly-dragon211/TOSS.

Related papers

• Are Decoder-Only Large Language Models the Silver Bullet for Code Search? [32.338318300589776]
  This study presents the first systematic exploration of decoder-only large language models for code search. We evaluate nine state-of-the-art decoder-only models using two fine-tuning methods, two datasets, and three model sizes. Our findings reveal that fine-tuned CodeGemma significantly outperforms encoder-only models like UniXcoder.
  arXiv  Detail & Related papers  (2024-10-29T17:05:25Z)
• How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval? [99.87554379608224]
  Cross-modal similarity score distribution of cross-encoder is more concentrated while the result of dual-encoder is nearly normal. Only the relative order between hard negatives conveys valid knowledge while the order information between easy negatives has little significance. We propose a novel Contrastive Partial Ranking Distillation (DCPR) method which implements the objective of mimicking relative order between hard negative samples with contrastive learning.
  arXiv  Detail & Related papers  (2024-07-10T09:10:01Z)
• Can the Query-based Object Detector Be Designed with Fewer Stages? [15.726619371300558]
  We propose a novel model called GOLO, which follows a two-stage decoding paradigm. Compared to other mainstream query-based models with multi-stage decoders, our model employs fewer decoder stages while still achieving considerable performance.
  arXiv  Detail & Related papers  (2023-09-28T09:58:52Z)
• Improving Code Search with Hard Negative Sampling Based on Fine-tuning [15.341959871682981]
  We introduce a cross-encoder architecture for code search that jointly encodes the concatenation of query and code. We also introduce a Retriever-Ranker (RR) framework that cascades the dual-encoder and cross-encoder to promote the efficiency of evaluation and online serving.
  arXiv  Detail & Related papers  (2023-05-08T07:04:28Z)
• Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization [60.91600465922932]
  We present an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoder. Our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods.
  arXiv  Detail & Related papers  (2022-10-23T00:32:04Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• Faster Person Re-Identification [68.22203008760269]
  We introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine hashing code search strategy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8% more accurate but also 5x faster than contemporary hashing ReID methods.
  arXiv  Detail & Related papers  (2020-08-16T03:02:49Z)
• Auto-Encoding Twin-Bottleneck Hashing [141.5378966676885]
  This paper proposes an efficient and adaptive code-driven graph. It is updated by decoding in the context of an auto-encoder. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods.
  arXiv  Detail & Related papers  (2020-02-27T05:58:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.