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检索条件"主题词=code summarization"

共 93 条记录，以下是41-50 订阅

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A Multi-Modal Transformer-based code summarization Approach for Smart Contracts 29

A Multi-Modal Transformer-based Code Summarization Approach ...

引用

29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Yang, Zhen Keung, Jacky Yu, Xiao Gu, Xiaodong Wei, Zhengyuan Ma, Xiaoxue Zhang, Miao City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781665414036

code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code. However, high-quality code comments are often unavailable in smart contracts, the increasingly popular programs that run on the blockchain. In this paper, we propose a Multi-Modal Transformer-based (MMTrans) code summarization approach for smart contracts. Specifically, the MMTrans learns the representation of source code from the two heterogeneous modalities of the Abstract Syntax Tree (AST), i.e., Structure-based Traversal (SBT) sequences and graphs. The SBT sequence provides the global semantic information of AST, while the graph convolution focuses on the local details. The MMTrans uses two encoders to extract both global and local semantic information from the two modalities respectively, and then uses a joint decoder to generate code comments. Both the encoders and the decoder employ the multi-head attention structure of the Transformer to enhance the ability to capture the long-range dependencies between code tokens. We build a dataset with over 300K pairs of smart contracts, and evaluate the MMTrans on it. The experimental results demonstrate that the MMTrans outperforms the state-of-the-art baselines in terms of four evaluation metrics by a substantial margin, and can generate higher quality comments.

关键词： Smart Contracts code summarization Transformer Graph Convolution Structure-based Traversal

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Active Learning for Low-Resource Project-Specific code summarization 1

引用

17th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Xing, Chengli Hu, Tianxiang Liao, Ninglin Zhang, Minghui Du, Dongdong Wu, Yupeng Gao, Qing Peking Univ Beijing Peoples R China Beijing Inst Control & Elect Technol Beijing Peoples R China Peking Univ Handan Inst Innovat Beijing Peoples R China China Acad Ind Internet Beijing Peoples R China

ISBN: (数字)9789819754892

ISBN: (纸本)9789819754885;9789819754892

code summarization aims to condense source code into concise and efficient summaries, crucial for enhancing code comprehension and maintainability. Deep learning and transfer learning techniques have significantly advanced state-of-the-art code summarization. However, constructing high-quality training datasets for these models remains challenging due to the high cost of manual annotation. Active learning methods offer a promising solution by intelligently selecting informative samples for annotation, reducing annotation costs while improving model performance. This paper explores the integration of active learning techniques into code summarization tasks, emphasizing the need to balance sample diversity and uncertainty in sample selection. Inspired by recent research, a novel active learning framework is proposed, leveraging both uncertainty and diversity metrics to construct effective training datasets. This framework provides a foundation for future research in project-specific code summarization.

关键词： Software Engineering code summarization Deep Learning Transfer Learning Active Learning

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Advances in code summarization 21

Advances in Code Summarization

引用

IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

作者： Desai, Utkarsh Sridhara, Giriprasad Tamilselvam, Srikanth IBM Res Bangalore Karnataka India

ISBN: (纸本)9781665412193

Several studies have suggested that comments describing source code can help mitigate the burden of program understanding. However, software systems usually lack adequate comments and sometime even when present, they may be outdated. Researchers have addressed this issue by automatically generating comments from source code, a task referred to as code summarization. In this technical presentation, we take a deeper look at some of the significant, recent works in the area of code summarization and how each of them attempts to take a new perspective of this task including methods leveraging RNNs, Transformers, Graph neural networks and Reinforcement learning. We review individual methods in detail and discuss future avenues for this task.

关键词： code summarization neural networks

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On the Evaluation of Neural code summarization 22

On the Evaluation of Neural Code Summarization

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ACM/IEEE 44th International Conference on Software Engineering (ICSE)

作者： Shi, Ensheng Wang, Yanlin Du, Lun Chen, Junjie Han, Shi Zhang, Hongyu Zhang, Dongmei Sun, Hongbin Xi An Jiao Tong Univ Xian Peoples R China Microsoft Res Beijing Peoples R China Tianjin Univ Tianjin Peoples R China Univ Newcastle Callaghan NSW Australia Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781450392211

Source code summaries are important for program comprehension and maintenance. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited to automatically generate summaries for given code snippets. To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets. The evaluation results show that some important factors have a great influence on the model evaluation, especially on the performance of models and the ranking among the models. However, these factors might be easily overlooked. Specifically, (1) the BLEU metric widely used in existing work of evaluating code summarization models has many variants. Ignoring the differences among these variants could greatly affect the validity of the claimed results. Besides, we discover and resolve an important and previously unknown bug in BLEU calculation in a commonly-used software package. Furthermore, we conduct human evaluations and find that the metric BLEU-DC is most correlated to human perception;(2) code preprocessing choices can have a large (from -18% to +25%) impact on the summarization performance and should not be neglected. We also explore the aggregation of pre-processing combinations and boost the performance of models;(3) some important characteristics of datasets (corpus sizes, data splitting methods, and duplication ratios) have a significant impact on model evaluation. Based on the experimental results, we give actionable suggestions for evaluating code summarization and choosing the best method in different scenarios. We also build a shared code summarization toolbox to facilitate future research.

关键词： code summarization Empirical study Deep learning Evaluation

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Revisiting Information Retrieval and Deep Learning Approaches for code summarization 23

Revisiting Information Retrieval and Deep Learning Approache...

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45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Zhu, Tingwei Li, Zhong Pan, Minxue Shi, Chaoxuan Zhang, Tian Pei, Yu Li, Xuandong Nanjing Univ Nanjing Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350322637

code summarization refers to the procedure of creating short descriptions that outline the semantics of source code snippets. Existing code summarization approaches can be broadly classified into Information Retrieval (IR)-based and Deep Learning (DL)-based approaches. However, their effectiveness, and especially their strengths and weaknesses, remain largely understudied. Existing evaluations use different benchmarks and metrics, making performance comparisons of these approaches susceptible to bias and potentially yielding misleading results. For example, the DL-based approaches typically show better code summarization performance in their original papers [1], [2]. However, Gros et al. [3] report that a naive IR approach could achieve comparable (or even better) performance to the DL-based ones. In addition, some recent work [4], [5] suggests that incorporating IR techniques can improve the DL-based approaches. To further advance code summarization techniques, it is critical that we have a good understanding of how IR-based and DL-based approaches perform on different datasets and in terms of different metrics. Prior works have studied some aspects of code summarization, such as the factors affecting performance evaluation [6] and the importance of data preprocessing [7], etc. In this paper, we focus on the study of the IR-based and DL-based code summarization approaches to enhance the understanding and design of more advanced techniques. We first compare the IRbased and DL-based approaches under the same experimental settings and benchmarks, then study their strengths and limitations through quantitative and qualitative analyses. Finally, we propose a simpler but effective strategy to combine IR and DL to further improve code summarization. Four IR-based approaches and two DL-based approaches are investigated with regard to representativeness and diversity. For IR-based approaches, we select three BM25-based approaches (i.e., BM25-spl, BM25-ast, BM25-alpha) and o

关键词： code summarization deep learning empirical study information retrieval

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A code summarization Approach for Object Oriented Programs

A Code Summarization Approach for Object Oriented Programs

引用

International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Mohsin, Ali Hameed Hammad, Mustafa Univ Bahrain Coll IT Sukhair Bahrain

ISBN: (纸本)9781728130125

The software maintenance process is not an easy job, especially when the source code becomes more complex and less documented. Software developers and engineers may spend plenty of time to understand the structures and features of the source code in order to maintain their software projects. Using an automated code summarization technique is a key factor to save time and cost of the maintenance process. Generating descriptive documents from the source code helps the software developers to understand their software. In this paper, a code summarization framework is proposed to document the source code. A set of software features are extracted and displayed to the developers based on mapping the target source code segments to an XML representation. The proposed framework has been conducted on an open source project to evaluate its effectiveness. The generated results showed that the proposed approach is useful in understanding the different structural aspects of the source code.

关键词： code summarization software documentation method summarization Reverse engineering

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Are We Building on the Rock? On the Importance of Data Preprocessing for code summarization 2022

Are We Building on the Rock? On the Importance of Data Prepr...

引用

30th ACM Joint European Software Engineering Conference / Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Shi, Lin Mu, Fangwen Chen, Xiao Wang, Song Wang, Junjie Yang, Ye Li, Ge Xia, Xin Wang, Qing Chinese Acad Sci Inst Software Beijing Peoples R China York Univ Lassonde Sch Engn Toronto ON Canada Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ USA Peking Univ Key Lab High Confidence Software Technol Beijing Peoples R China Huawei Software Engn Applicat Technol Lab Beijing Peoples R China Chinese Acad Sci Inst Software Lab Internet Software Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Sci Technol Integrated Informat Syst Lab Beijing Peoples R China

ISBN: (纸本)9781450394130

code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice.

关键词： code summarization Data Quality Empirical Study

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Improving code summarization with Block-wise Abstract Syntax Tree Splitting 29

Improving Code Summarization with Block-wise Abstract Syntax...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Lin, Chen Ouyang, Zhichao Zhuang, Junqing Chen, Jianqiang Li, Hui Wu, Rongxin Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9781665414036

Automatic code summarization frees software developers from the heavy burden of manual commenting and benefits software development and maintenance. Abstract Syntax Tree (AST), which depicts the source code's syntactic structure, has been incorporated to guide the generation of code summaries. However, existing AST based methods suffer from the difficulty of training and generate inadequate code summaries. In this paper, we present the Block-wise Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes the rich tree-form syntax structure in ASTs, for improving code summarization. BASTS splits the code of a method based on the blocks in the dominator tree of the Control Flow Graph, and generates a split AST for each code split. Each split AST is then modeled by a Tree-LSTM using a pre-training strategy to capture local non-linear syntax encoding. The learned syntax encoding is combined with code encoding, and fed into Transformer to generate high-quality code summaries. Comprehensive experiments on benchmarks have demonstrated that BASTS significantly outperforms state-of-the-art approaches in terms of various evaluation metrics. To facilitate reproducibility, our implementation is available at https://***/XMUDM/BASTS.

关键词： code summarization code Splitting Abstract Syntax Tree

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Using Developer Eye Movements to Externalize the Mental Model Used in code summarization Tasks 19

Using Developer Eye Movements to Externalize the Mental Mode...

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11th ACM Symposium On Eye Tracking Research and Applications (ETRA)

作者： Abid, Nahla J. Maletic, Jonathan I. Sharif, Bonita Taibah Univ Dept Comp Sci Madinah Saudi Arabia Kent State Univ Dept Comp Sci Kent OH 44240 USA Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781450367097

Eye movements of developers are used to speculate the mental cognition model (i.e., bottom-up or top-down) applied during program comprehension tasks. The cognition models examine how programmers understand source code by describing the temporary information structures in the programmer's short term memory. The two types of models that we are interested in are top-down and bottom-up. The top-down model is normally applied as-needed (i.e., the domain of the system is familiar). The bottom-up model is typically applied when a developer is not familiar with the domain or the source code. An eye-tracking study of 18 developers reading and summarizing Java methods is used as our dataset for analyzing the mental cognition model. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. The results indicate that on average, experts and novices read the methods more closely (using the bottom-up mental model) than bouncing around (using top-down). However, on average novices spend longer gaze time performing bottom-up (66s.) compared to experts (43s.)

关键词： program comprehension eye tracking study code summarization

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iiPCS: Intent-Based In-Context Learning for Project-Specific code summarization

iiPCS: Intent-Based In-Context Learning for Project-Specific...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Yu Liu, Xin Lu, Xuesong Zhou, Aoying East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Recent years have witnessed growing research interest in automatic source code summarization due to its beneficial potential in software development and maintenance tasks. In the past few years, various deep learning models have been developed to leverage structural and textual features in the code for generating meaningful and succinct summaries. However, the summaries generated by traditional deep learning models often have syntax errors or are meaningless. The emergence of large language models provides an opportunity to overcome the problem. However, the quality of the summaries largely depends on the in-context learning examples of code-summary pairs. In this work, we develop iiPCS, an LLM-based method for code summarization. We retrieve relevant code-summary pairs as incontext learning examples from the same project of the target code, which ensures to generate more project-specific summaries, and use the predicted intent of the target code to pick few-shot examples, which ensures to generate summaries with the correct intent. Experimental results show that iiPCS can generate code summaries with higher quality compared to traditional methods using deep learning and recent methods using LLMs.

关键词： code summarization Large Language Models Project-specific Summaries In-context Learning Developer Intent

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