CodeRetriever: Unimodal and Bimodal Contrastive Learning

• URL: http://arxiv.org/abs/2201.10866v1
• Date: Wed, 26 Jan 2022 10:54:30 GMT
• Title: CodeRetriever: Unimodal and Bimodal Contrastive Learning
• Authors: Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan
• Abstract summary: We propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations. For unimodal contrastive learning, we design a semantic-guided method to build positive code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs.
• Score: 128.06072658302165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations, specifically for the code search task. For unimodal contrastive learning, we design a semantic-guided method to build positive code pairs based on the documentation and function name. For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build text-code pairs. Both contrastive objectives can fully leverage the large-scale code corpus for pre-training. Experimental results on several public benchmarks, (i.e., CodeSearch, CoSQA, etc.) demonstrate the effectiveness of CodeRetriever in the zero-shot setting. By fine-tuning with domain/language specified downstream data, CodeRetriever achieves the new state-of-the-art performance with significant improvement over existing code pre-trained models. We will make the code, model checkpoint, and constructed datasets publicly available.

Related papers

• CodeExp: Explanatory Code Document Generation [94.43677536210465]
  Existing code-to-text generation models produce only high-level summaries of code. We conduct a human study to identify the criteria for high-quality explanatory docstring for code. We present a multi-stage fine-tuning strategy and baseline models for the task.
  arXiv  Detail & Related papers  (2022-11-25T18:05:44Z)
• Soft-Labeled Contrastive Pre-training for Function-level Code Representation [127.71430696347174]
  We present textbfSCodeR, a textbfSoft-labeled contrastive pre-training framework with two positive sample construction methods. Considering the relevance between codes in a large-scale code corpus, the soft-labeled contrastive pre-training can obtain fine-grained soft-labels. SCodeR achieves new state-of-the-art performance on four code-related tasks over seven datasets.
  arXiv  Detail & Related papers  (2022-10-18T05:17:37Z)
• 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)
• ReACC: A Retrieval-Augmented Code Completion Framework [53.49707123661763]
  We propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval. We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
  arXiv  Detail & Related papers  (2022-03-15T08:25:08Z)
• GraphCodeBERT: Pre-training Code Representations with Data Flow [97.00641522327699]
  We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. We use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement.
  arXiv  Detail & Related papers  (2020-09-17T15:25:56Z)
• Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations [28.61567319928316]
  Corder is a self-supervised contrastive learning framework for source code model. Key innovation is that we train the source code model by asking it to recognize similar and dissimilar code snippets. We have shown that the code models pretrained by Corder substantially outperform the other baselines for code-to-code retrieval, text-to-code retrieval, and code-to-text summarization tasks.
  arXiv  Detail & Related papers  (2020-09-06T13:31:16Z)
• Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning [18.354352985591305]
  Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Recent studies have combined these two tasks to improve their performance. We propose a novel end-to-end model for the two tasks by introducing an additional code generation task.
  arXiv  Detail & Related papers  (2020-02-24T12:26:11Z)

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.