检索结果-内蒙古大学图书馆

PyScribe-Learning to describe python code

SOFTWARE-PRACTICE & EXPERIENCE 2024年第3期54卷 501-527页

作者： Guo, Juncai Liu, Jin Liu, Xiao Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyande, Tegawende Wuhan Univ Sch Comp Sci Wuhan Peoples R China Chinese Acad Sci Inst Informat Engn Key Lab Network Assessment Technol Beijing Peoples R China Deakin Univ Sch Informat Technol Melbourne Australia Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Monash Univ Fac Informat Technol Clayton Vic Australia Beihang Univ Sch Software Beijing Peoples R China Huawei Software Engn Applicat Technol LabHangzhou Hangzhou Peoples R China Univ Luxembourg SnT Ctr Esch Sur Alzette Luxembourg

code comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a pro-to type tool titled PyScribe, which extends the Transformer model to a new encoder-decoder-based framework. Particularly, the triplet position is designed and integrated into the node-level and edge-level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two-stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe, we resort to a large dataset of code-comment pairsby mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state-of the-art by achieving an average BLEU score (i.e., av-BLEU) of approximate to 0.28.

关键词： code comprehension code documentation code embedding code summarization deep learning representation learning

来源：评论

学校读者我要写书评

暂无评论

Multilingual training for Software Engineering 22

Multilingual training for Software Engineering

引用

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

作者： Ahmed, Toufique Devanbu, Premkumar Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781450392211

Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better models and training methods. More, and more diverse, clean, labeled data is better for training;but constructing good-quality datasets is timeconsuming and challenging. Ways of augmenting the volume and diversity of clean, labeled data generally have wide applicability. For some languages (e.g., Ruby) labeled data is less abundant;in others (e.g., JavaScript) the available data maybe more focused on some application domains, and thus less diverse. As a way around such data bottlenecks, we present evidence suggesting that human-written code in different languages (which performs the same function), is rather similar, and particularly preserving of identifier naming patterns;we further present evidence suggesting that identifiers are a very important element of training data for software engineering tasks. We leverage this rather fortuitous phenomenon to find evidence that available multilingual training data (across different languages) can be used to amplify performance. We study this for 3 different tasks: code summarization, code retrieval, and function naming. We note that this data-augmenting approach is broadly compatible with different tasks, languages, and machine-learning models.

关键词： code summarization code search method name prediction deep learning

来源：评论

学校读者我要写书评

暂无评论

A Fine-Grained Approach for Automated Conversion of JUnit Assertions to English 4

A Fine-Grained Approach for Automated Conversion of JUnit As...

引用

4th ACM SIGSOFT International Workshop on NLP for Software Engineering (NL4SE)

作者： Gonzalez, Danielle Prentice, Suzanne Mirakhorli, Mehdi Rochester Inst Technol Rochester NY 14623 USA Univ South Carolina Columbia SC 29208 USA

ISBN: (纸本)9781450360555

Converting source or unit test code to English has been shown to improve the maintainability, understandability, and analysis of software and tests. code summarizers identify 'important' statements in the source/tests and convert them to easily understood English sentences using static analysis and NLP techniques. However, current test summarization approaches handle only a subset of the variation and customization allowed in the JUnit assert API (a critical component of test cases) which may affect the accuracy of conversions. In this paper, we present our work towards improving JUnit test summarization with a detailed process for converting a total of 45 unique JUnit assertions to English, including 37 previously-unhandled variations of the assertThat method. This process has also been implemented and released as the AssertConvert tool. Initial evaluations have shown that this tool generates English conversions that accurately represent a wide variety of assertion statements which could be used for code summarization or other NLP analyses.

关键词： Software Testing JUnit code summarization NLP

来源：评论

学校读者我要写书评

暂无评论

Summarizing Source code from Structure and Context

Summarizing Source Code from Structure and Context

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Hou, Shifu Chen, Lingwei Ye, Yanfang Case Western Reserve Univ Cleveland OH USA Wright State Univ Dayton OH USA Univ Notre Dame Notre Dame IN USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Modern software developers tend to engage in social coding platforms to reuse code snippets to expedite the development process, while the codes on such platforms are often suffering from comments being mismatched, missing or outdated. This puts the code search and comprehension in difficulty, and increases the burden of maintenance for software building upon these codes. As summarizing code is beneficial yet it is very expensive for manual operation, in this paper, we elaborate an automatic and effective code summarization paradigm to address this laborious challenge. We represent a given code snippet as an abstract syntax tree (AST), and generate a set of compositional root-to-leaf paths to make the AST accessible regarding code context and structure in a less complex yet expressive way. Accordingly, we design a tree-based transformer model, called TreeXFMR, on these paths to summarize source code in a hierarchical attention operation. This yields two advantages on code representation learning: (1) attention mechanisms at token- and path-level attend the semantics and interactions of source code from different aspects;(2) bi-level positional encodings introduced reveal the intra- and inter-path structure of AST and improve the unambiguity of the representations. During decoding, TreeXFMR attends such learned representations to produce each output of natural language word. We further pre-train the transformer to achieve faster and better training convergence results. Extensive experiments on the code collection from GitHub demonstrate the effectiveness of TreeXFMR, which significantly outperforms state-of-the-art baselines.

关键词： code summarization abstract syntax tree natural machine translation transformer

来源：评论

学校读者我要写书评

暂无评论

Capturing Source code Semantics via Tree-based Convolution over API-enhanced AST 19

Capturing Source Code Semantics via Tree-based Convolution o...

引用

16th ACM International Conference on Computing Frontiers (CF)

作者： Chen, Long Ye, Wei Zhang, Shikun Peking Univ Beijing Peoples R China

ISBN: (纸本)9781450366854

When deep learning meets big code, a key question is how to efficiently learn a distributed representation for source code that can capture its semantics effectively. We propose to use tree-based convolution over API-enhanced AST. To demonstrate the effectiveness of our approach, we apply it to detect semantic clones-code fragments with similar semantics but dissimilar syntax. Experiment results show that our approach outperforms an existing state-of-the-art approach that uses tree-based LSTM, with an increase of 0.39 and 0.12 in F1-score on OJClone and BigCloneBench respectively. We further propose architectures that incorporate our approach for code search and code summarization.

关键词： tree-based convolution tree-based LSTM representation learning big code code semantics AST API semantic clone clone detection code search code summarization

来源：评论

学校读者我要写书评

暂无评论

Parameter Description Generation with the code Parameter Flow 22

Parameter Description Generation with the Code Parameter Flo...

引用

22nd IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Chen, Qiuyuan Yang, Zezhou Liu, Zhongxin Li, Shanping Gao, Cuiyun Zhejiang Univ Hangzhou Zhejiang Peoples R China Harbin Inst Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665477048

Prior study shows that comprehending parameters can help developers understand the code's critical information (e.g., the argument) and enhance the comprehension of the functionality. However, commenting parameter is often ignored in practice. For example, a statistic of 18 popular open-source projects shows the ratio of methods with one or more parameters but lacking "@param" comment ranges from 31% to 97%, indicating the necessity of parameter comments. To fill this gap, we propose ParamDesGen to generate a descriptive code comment (description) for each parameter given a method with one or more formal parameters. ParamDesGen consists of (1) a code analysis component to identify the Parameter Flow and extract "parameter-related code parts" and (2) a machine-learning component to generate parameter comments. We build a large-scale dataset for the task and perform experiments on it to evaluate ParamDesGen. The evaluation results show that the proposed approach substantially outperforms the baselines in terms of BLEU-4 scores (22.54 absolute improvement and 138.79% relative improvement) and ROUGE-L scores (3.12 absolute improvement and 5.90% relative improvement). We further perform ablation experiments to prove the effectiveness of the Parameter Flow.

关键词： Parameter Description Parameter Flow code summarization Comment Generation

来源：评论

学校读者我要写书评

暂无评论

CNSVRE: A Query Reformulated Search System with Explainable summarization for Virtual Research Environment

CNSVRE: A Query Reformulated Search System with Explainable ...

引用

32nd World Wide Web Conference (WWW)

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Univ Amsterdam Informat Inst Amsterdam Netherlands

ISBN: (纸本)9781450394161

Computational notebook environments have drawn broad attention in data-centric research applications, e.g., virtual research environment, for exploratory data analysis and algorithm prototyping. Vanilla computational notebook search solutions have been proposed but they do not pay much attention to the information needs of scientific researchers. Previous studies either treat computational notebook search as a code search problem or focus on content-based computational notebook search. The queries being considered are neither research-concerning nor diversified whereas researchers' information needs are highly specialized and complex. Moreover, relevance evaluation for computational notebooks is tricky and unreliable since computational notebooks contain fragments of text and code and are usually poorly organized. To solve the above challenges, we propose a computational notebook search system for virtual research environment (VRE), i.e., CNSVRE, with scientific query reformulation and computational notebook summarization. We conduct a user study to demonstrate the effectiveness, efficiency, and satisfaction with the system.

关键词： computational notebook search virtual research environment scientific query reformulation code summarization

来源：评论

学校读者我要写书评

暂无评论

Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow 21

Leveraging Unsupervised Learning to Summarize APIs Discussed...

引用

21st IEEE International Working Conference on Source code Analysis and Manipulation (SCAM)

作者： Naghshzan, AmirHossein Guerrouj, Latifa Baysal, Olga Ecole Technol Super Montreal PQ Canada Carleton Univ Ottawa ON Canada

ISBN: (纸本)9781665448970

Automated source code summarization is a task that generates summarized information about the purpose, usage, and-or implementation of methods and classes to support understanding of these code entities. Multiple approaches and techniques have been proposed for supervised and unsupervised learning in code summarization, however, they were mostly focused on generating a summary for a piece of code. In addition, very few works have leveraged unofficial documentation. This paper proposes an automatic and novel approach for summarizing Android API methods discussed in Stack Overflow that we consider as unofficial documentation in this research. Our approach takes the API method's name as an input and generates a natural language summary based on Stack Overflow discussions of that API method. We have conducted a survey that involves 16 Android developers to evaluate the quality of our automatically generated summaries and compare them with the official Android documentation. Our results demonstrate that while developers find the official documentation more useful in general, the generated summaries are also competitive, in particular for offering implementation details, and can be used as a complementary source for guiding developers in software development and maintenance tasks.

关键词： code summarization unsupervised learning unofficial documentation survey professional developers

来源：评论

学校读者我要写书评

暂无评论

A Gaze-Based Exploratory Study on the Information Seeking Behavior of Developers on Stack Overflow

A Gaze-Based Exploratory Study on the Information Seeking Be...

引用

CHI Conference on Human Factors in Computing Systems (CHI)

作者： Peterson, Cole S. Saddler, Jonathan A. Halavick, Natalie M. Sharif, Bonita Univ Nebraska Lincoln NE 68588 USA Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450359719

Software developers use Stack Overflow on a daily basis to search for solutions to problems they encounter during bug fixing and feature enhancement. In prior work, studies have been done on mining Stack Overflow data such as for predicting unanswered questions or how and why people post. However, no work exists on how developers actually use, or more importantly, read the information presented to them on Stack Overflow. To better understand this behavior, we conduct an eye tracking study on how developers seek for information on Stack Overflow while tasked with creating human-readable summaries of methods and classes in large Java projects. Eye gaze data is collected on both the source code elements and Stack Overflow document elements at a fine token-level granularity using iTrace, our eye tracking infrastructure. We found that developers look at the text more often than the title in posts. code snippets were the second most looked at element. Tags and votes are rarely looked at. When switching between Stack Overflow and the Eclipse Integrated Development Environment (IDE), developers often looked at method signatures and then switched to code and text elements on Stack Overflow. Such heuristics provide insight to automated code summarization tools as they decide what to give more weight to while generating summaries.

关键词： eye tracking study Stack Overflow source code reading behavior code summarization

来源：评论

学校读者我要写书评

暂无评论

Implant Global and Local Hierarchy Information to Sequence based code Representation Models 31

Implant Global and Local Hierarchy Information to Sequence b...

引用

31st IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Zhang, Kechi Li, Zhuo Jin, Zhi Li, Ge Peking Univ MoE Key Lab High Confidence Software Technol Beijing Peoples R China

ISBN: (纸本)9798350337501

Source code representation with deep learning techniques is an important research field. There have been many studies that learn sequential or structural information for code representation. But sequence-based models and non-sequence-models both have their limitations. Researchers attempt to incorporate structural information to sequence-based models, but they only mine part of token-level hierarchical structure information. In this paper, we analyze how the complete hierarchical structure influences the tokens in code sequences and abstract this influence as a property of code tokens called hierarchical embedding. The hierarchical embedding is further divided into statement-level global hierarchy and token-level local hierarchy. Furthermore, we propose the Hierarchy Transformer (HiT), a simple but effective sequence model to incorporate the complete hierarchical embeddings of source code into a Transformer model. We demonstrate the effectiveness of hierarchical embedding on learning code structure with an experiment on variable scope detection task. Further evaluation shows that HiT outperforms SOTA baseline models and show stable training efficiency on three source code-related tasks involving classification and generation tasks across 8 different datasets.

关键词： code Representation code summarization code Classification Clone Detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：