CoNCRA: A Convolutional Neural Network Code Retrieval Approach

• URL: http://arxiv.org/abs/2009.01959v1
• Date: Thu, 3 Sep 2020 23:38:52 GMT
• Title: CoNCRA: A Convolutional Neural Network Code Retrieval Approach
• Authors: Marcelo de Rezende Martins and Marco A. Gerosa
• Abstract summary: We propose a technique for semantic code search: A Convolutional Neural Network approach to code retrieval. Our technique aims to find the code snippet that most closely matches the developer's intent, expressed in natural language. We evaluated our approach's efficacy on a dataset composed of questions and code snippets collected from Stack Overflow.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software developers routinely search for code using general-purpose search engines. However, these search engines cannot find code semantically unless it has an accompanying description. We propose a technique for semantic code search: A Convolutional Neural Network approach to code retrieval (CoNCRA). Our technique aims to find the code snippet that most closely matches the developer's intent, expressed in natural language. We evaluated our approach's efficacy on a dataset composed of questions and code snippets collected from Stack Overflow. Our preliminary results showed that our technique, which prioritizes local interactions (words nearby), improved the state-of-the-art (SOTA) by 5% on average, retrieving the most relevant code snippets in the top 3 (three) positions by almost 80% of the time. Therefore, our technique is promising and can improve the efficacy of semantic code retrieval.

Related papers

• NS3: Neuro-Symbolic Semantic Code Search [33.583344165521645]
  We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.
  arXiv  Detail & Related papers  (2022-05-21T20:55:57Z)
• 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)
• Accelerating Code Search with Deep Hashing and Code Classification [64.3543949306799]
  Code search is to search reusable code snippets from source code corpus based on natural languages queries. We propose a novel method CoSHC to accelerate code search with deep hashing and code classification.
  arXiv  Detail & Related papers  (2022-03-29T07:05:30Z)
• 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)
• Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus [17.6095840480926]
  We propose a novel deep semantic model which makes use of the utilities of multi-modal sources. We apply the proposed model to tackle the CodeSearchNet challenge about semantic code search. Our model is trained on CodeSearchNet corpus and evaluated on the held-out data, the final model achieves 0.384 NDCG and won the first place in this benchmark.
  arXiv  Detail & Related papers  (2022-01-27T04:15:59Z)
• Deep Graph Matching and Searching for Semantic Code Retrieval [76.51445515611469]
  We propose an end-to-end deep graph matching and searching model based on graph neural networks. We first represent both natural language query texts and programming language code snippets with the unified graph-structured data. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them.
  arXiv  Detail & Related papers  (2020-10-24T14:16:50Z)
• COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
  We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic. COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:53:38Z)
• Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent [1.1168121941015012]
  We study how code retrieval systems can be improved by leveraging descriptions to better capture the intents of code snippets. Building on recent progress in transfer learning and natural language processing, we create a domain-specific retrieval model for code annotated with a natural language description.
  arXiv  Detail & Related papers  (2020-08-27T15:39:09Z)
• Contrastive Code Representation Learning [95.86686147053958]
  We show that the popular reconstruction-based BERT model is sensitive to source code edits, even when the edits preserve semantics. We propose ContraCode: a contrastive pre-training task that learns code functionality, not form.
  arXiv  Detail & Related papers  (2020-07-09T17:59:06Z)

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.