ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization

• URL: http://arxiv.org/abs/2407.01646v1
• Date: Mon, 1 Jul 2024 03:06:51 GMT
• Title: ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization
• Authors: Chunrong Fang, Weisong Sun, Yuchen Chen, Xiao Chen, Zhao Wei, Quanjun Zhang, Yudu You, Bin Luo, Yang Liu, Zhenyu Chen,
• Abstract summary: Code summarization aims to automatically generate succinct natural language summaries for given code snippets. This paper proposes a novel approach to improve code summarization based on summary-focused tasks.
• Score: 21.886950861445122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: (Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting developers to understand and maintain code. Inspired by neural machine translation, deep learning-based code summarization techniques widely adopt an encoder-decoder framework, where the encoder transforms given code snippets into context vectors, and the decoder decodes context vectors into summaries. Recently, large-scale pre-trained models for source code are equipped with encoders capable of producing general context vectors and have achieved substantial improvements on code summarization. However, although they are usually trained mainly on code-focused tasks and can capture general code features, they still fall short in capturing specific features that need to be summarized. This paper proposes a novel approach to improve code summarization based on summary-focused tasks. Specifically, we exploit a multi-task learning paradigm to train the encoder on three summary-focused tasks to enhance its ability to learn code-summary alignment, including unidirectional language modeling (ULM), masked language modeling (MLM), and action word prediction (AWP). Unlike pre-trained models that mainly predict masked tokens in code snippets, we design ULM and MLM to predict masked words in summaries. Intuitively, predicting words based on given code snippets would help learn the code-summary alignment. Additionally, we introduce the domain-specific task AWP to enhance the ability of the encoder to learn the alignment between action words and code snippets. The extensive experiments on four datasets demonstrate that our approach, called ESALE significantly outperforms baselines in all three widely used metrics, including BLEU, METEOR, and ROUGE-L.

Related papers

• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Zero-Shot Code Representation Learning via Prompt Tuning [6.40875582886359]
  We propose Zecoler, a zero-shot approach for learning code representations. Zecoler is built upon a pre-trained programming language model. We evaluate Zecoler in five code intelligence tasks including code clone detection, code search, method name prediction, code summarization, and code generation.
  arXiv  Detail & Related papers  (2024-04-13T09:47:07Z)
• SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization [51.67317895094664]
  This paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. We propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences.
  arXiv  Detail & Related papers  (2024-01-26T09:23:27Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)
• 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)
• An Extractive-and-Abstractive Framework for Source Code Summarization [28.553366270065656]
  Code summarization aims to automatically generate summaries/comments for a given code snippet in the form of natural language. We propose a novel extractive-and-abstractive framework to generate human-written-like summaries with preserved factual details.
  arXiv  Detail & Related papers  (2022-06-15T02:14:24Z)
• GypSum: Learning Hybrid Representations for Code Summarization [21.701127410434914]
  GypSum is a new deep learning model that learns hybrid representations using graph attention neural networks and a pre-trained programming and natural language model. We modify the encoder-decoder sublayer in the Transformer's decoder to fuse the representations and propose a dual-copy mechanism to facilitate summary generation.
  arXiv  Detail & Related papers  (2022-04-26T07:44:49Z)
• CLSEBERT: Contrastive Learning for Syntax Enhanced Code Pre-Trained Model [23.947178895479464]
  We propose CLSEBERT, a Constrastive Learning Framework for Syntax Enhanced Code Pre-Trained Model. In the pre-training stage, we consider the code syntax and hierarchy contained in the Abstract Syntax Tree (AST) We also introduce two novel pre-training objectives. One is to predict the edges between nodes in the abstract syntax tree, and the other is to predict the types of code tokens.
  arXiv  Detail & Related papers  (2021-08-10T10:08:21Z)
• Project-Level Encoding for Neural Source Code Summarization of Subroutines [6.939768185086755]
  We present a project-level encoder to improve models of code summarization. We use that representation to augment the encoder of state-of-the-art neural code summarization techniques.
  arXiv  Detail & Related papers  (2021-03-22T06:01:07Z)
• 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)
• A Transformer-based Approach for Source Code Summarization [86.08359401867577]
  We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
  arXiv  Detail & Related papers  (2020-05-01T23:29:36Z)

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.