检索结果-内蒙古大学图书馆

Multilingual code refactoring detection based on deep learning

EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Li, Tao Zhang, Yang Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Peoples R China

refactoring is a critical process of improving the internal structure of the source code without altering its external behavior. Existing deep learning-based refactoring detection relies on commit messages to extract features. However, these commit messages are not trustful enough since some developers do not consistently record refactoring activities. Furthermore, current approaches are designed for a single programming language and lack multilingual refactoring support. To this end, this paper proposes RefT5, , a multilingual code refactoring detection approach based on deep learning. Firstly, we select 110 real-world projects with Java and Python programming languages as a corpus to construct the dataset. Secondly, we extract features including commit messages, code changes, and refactoring types from these projects. RefT5 generates edit sequences from code changes and takes refactoring types as labels. Thirdly, we employ CodeT5 and BiLSTM-attention to extract semantic and structural features and generate feature vectors. Finally, the feature vectors are input into a classification layer to detect the refactoring type. The experimental results show that RefT5 obtains 98.05% precision and 97.77% recall. Furthermore, compared with existing approaches, it improves precision by 51.61% and recall by 52.9% on average, demonstrating its effectiveness.

关键词： refactoring detection Deep learning Code change Multilingual code Edit sequence

来源：评论

学校读者我要写书评

暂无评论

Impact of Change Granularity in refactoring detection 30

Impact of Change Granularity in Refactoring Detection

引用

30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Chen, Lei Hayashi, Shinpei Tokyo Inst Technol Meguro Ku Tokyo Japan

ISBN: (纸本)9781450392983

Detecting refactorings in commit history is essential to improve the comprehension of code changes in code reviews and to provide valuable information for empirical studies on software evolution. Several techniques have been proposed to detect refactorings accurately at the granularity level of a single commit. However, refactorings may be performed over multiple commits because of code complexity or other real development problems, which is why attempting to detect refactorings at single-commit granularity is insufficient. We observe that some refactorings can be detected only at coarser granularity, that is, changes spread across multiple commits. Herein, this type of refactoring is referred to as coarse-grained refactoring (CGR). We compared the refactorings detected on different granularities of commits from 19 open-source repositories. The results show that CGRs are common, and their frequency increases as the granularity becomes coarser. In addition, we found that Move-related refactorings tended to be the most frequent CGRs. We also analyzed the causes of CGR and suggested that CGRs will be valuable in refactoring research.

关键词： refactoring detection Squashed commit Git

来源：评论

学校读者我要写书评

暂无评论

RefDetect: A Multi-Language refactoring detection Tool Based on String Alignment

引用

IEEE ACCESS 2021年 9卷 86698-86727页

作者： Moghadam, Iman Hemati Cinneide, Mel O. Zarepour, Faezeh Jahanmir, Mohamad Aref Vali E Asr Univ Rafsanjan Dept Comp Engn Rafsanjan *** Iran Natl Univ Ireland Sch Comp Sci Dublin D04 V1W8 Ireland Allameh Jafari Inst Rafsanjan Dept Comp Engn Rafsanjan 77181 Iran

refactoring is performed to improve software quality while leaving the behaviour of the software unchanged. Identifying refactorings applied to a software system is an important activity that leads to a better understanding of the evolution of the software system, and several techniques have been proposed and implemented to address this issue. The vast majority of existing refactoring detection techniques are language-specific, including the accepted state of the art, RMiner, which is exclusively Java-based. Although impressive performance has been achieved to date, there is scope for improvement in refactoring detection and such improvement would enhance both refactoring research and practice. In this paper, we propose a novel, language-neutral technique to identify refactorings in commit histories. Our approach is motivated by a desire to explore the use of string alignment algorithms in refactoring detection, and to determine if such approaches are competitive with the state of the art. The proposed approach has been implemented in a tool called RefDetect, evaluated, and compared with the current state-of-the-art refactoring detection tool: RMiner. In experiments we applied RefDetect to 514 commits of 185 Java applications containing 5,058 true refactoring instances, achieving an f-score slightly better than that achieved by RMiner (87.3% vs. 86%). RefDetect clearly outperformed RMiner in method and class based refactorings, achieving f-scores respectively of 87.7% vs. 81.7% for method-level refactorings and 92.1% vs. 86.9% for class-level refactorings. To demonstrate the language-independence of RefDetect, we conducted a further study with four C++ applications, achieving high values for both precision (96.1%) and recall (94.1%). The achieved results indicate that RefDetect performs better than the current state of the art in refactoring detection and is demonstrably capable of handling different programming languages.

关键词： Tools Java Software C plus plus languages Measurement Unified modeling language Software algorithms refactoring refactoring detection alignment algorithm empirical studies Java C plus plus

来源：评论

学校读者我要写书评

暂无评论

PYREF: refactoring detection in Python Projects 21

PYREF: Refactoring Detection in Python Projects

引用

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

作者： Atwi, Hassan Lin, Bin Tsantalis, Nikolaos Kashiwa, Yutaro Kamei, Yasutaka Ubayashi, Naoyasu Bavota, Gabriele Lanza, Michele USI Software Inst Lugano Switzerland Concordia Univ Montreal PQ Canada Kyushu Univ Fukuoka Japan

ISBN: (纸本)9781665448970

refactoring, the process of improving the internal code structure of a software system without altering its external behavior, is widely applied during software development. Understanding how developers refactor source code can help gain better understanding of the software development process and the relationship between various versions of a system. refactoring detection tools have been developed for many popular programming languages, such as Java (e.g., refactoringMINER and REF-FINDER) but, quite surprisingly, this is not the case for Python, a widely used programming language. Inspired by refactoring MINER, we present PYREF, a tool that automatically detects method-level refactoring operations in Python projects. We evaluated PYREF against a manually built oracle and compared it with a PYTHON-ADAPTED refactoringMINER, which converts Python program to Java and detects refactoring operations with refactoring MINER. Our results indicate that PYREF can achieve satisfactory precision and detect more refactorings than the current state-of-the-art.

关键词： refactoring detection Python software maintenance

来源：评论

学校读者我要写书评

暂无评论

Detecting refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach

引用

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2025年第3期34卷 1-25页

作者： Noei, Shayan Li, Heng Zou, Ying Queens Univ Elect & Comp Engn Kingston ON Canada Queens Univ Sch Comp Kingston ON Canada Queens Univ Kingston ON Canada

refactoring aims to improve the quality of software without altering its functional behaviors. Understanding developers' refactoring activities is essential to improve software maintainability. The use of machine learning (ML) libraries and frameworks in software systems has significantly increased in recent years, making the maximization of their maintainability crucial. Due to the data-driven nature of ML libraries and frameworks, they often undergo a different development process compared to traditional projects. As a result, they may experience various types of refactoring, such as those related to the data. The state-of-the-art refactoring detection tools have not been tested in the ML technical domain, and they are not specifically designed to detect ML-specific refactoring types (e.g., data manipulation) in ML projects;therefore, they may not adequately find all potential refactoring operations, specifically the ML-specific refactoring operations. Furthermore, a vast number of ML libraries and frameworks are written in Python, which has limited tooling support for refactoring detection. PyRef, a rule-based and state-of-the-art tool for Python refactoring detection, can identify 11 types of refactoring operations with relatively high precision. In contrast, for other languages such as Java, state-of-the-art tools are capable of detecting a much more comprehensive list of refactorings. For example, Rminer can detect 99 types of refactoring for Java projects. Inspired by previous work that leverages commit messages to detect refactoring, we introduce MLRefScanner, a prototype tool that applies ML techniques to detect refactoring commits in ML Python projects. MLRefScanner detects commits involving both ML-specific refactoring operations and additional refactoring operations beyond the scope of state-of-the-art refactoring detection tools. To demonstrate the effectiveness of our approach, we evaluate MLRefScanner on 199 ML open source libraries and frameworks and

关键词： Code refactoring refactoring detection Python refactoring Machine Learning Code Quality

来源：评论

学校读者我要写书评

暂无评论

Enhancing Code Insights through Semantic Change Impact Evaluation 15

Enhancing Code Insights through Semantic Change Impact Evalu...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program. © 2024 IEEE.

关键词： Abstract Syntax Trees (ASTs) change detection refactoring detection semantic analysis semantic changes

来源：评论

学校读者我要写书评

暂无评论

The Three-Step refactoring Detector Pattern 19

The Three-Step Refactoring Detector Pattern

引用

24th European Conference on Pattern Languages of Programs (EuroPLoP)

作者： Tsimakis, Anastasios Zarras, Apostolos, V Vassiliadis, Panos Univ Ioannina Dept Comp Sci & Engn Ioannina Greece

ISBN: (纸本)9781450362061

Developing a tool that provides support for different refactorings, through a set of refactoring detectors which identify opportunities for source code improvements, is not easy. Our experience in developing such a tool for refactoring object-oriented software revealed the THREE-STEP refactoring DETECTOR pattern. The main idea behind the pattern is to develop an extensible hierarchy of refactoring detectors, with respect to a general three-step refactoring detection process. The proposed pattern facilitates the expansion of the hierarchy with new refactoring detectors and enables the reuse of existing refactoring detectors, provided by third party developers. Concerning maintainability, the pattern promotes the development of simple, clean and technology independent refactoring detectors. We have used the pattern for the development of 11 different refactoring detectors in the context of our tool. The pattern has not been observed in other contexts. However, the usage of the pattern in our tool brought out specific empirical evidence of its benefits, which we discuss in this paper.

关键词： refactoring refactoring detection Patterns

来源：评论

学校读者我要写书评

暂无评论

Comparative Study between Two Approaches Using Edit Operations and Code Differences to Detect Past refactorings

引用

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2018年第3期E101D卷 644-658页

作者： Omori, Takayuki Maruyama, Katsuhisa Ritsumeikan Univ Dept Comp Sci Kusatsu 5258577 Japan Ritsumeikan Univ Dept Informat Sci & Engn Kusatsu 5258577 Japan

Understanding which refactoring transformations were performed is in demand in modern software constructions. Traditionally, many researchers have been tackling understanding code changes with history data derived from version control systems. In those studies, problems of the traditional approach are pointed out, such as entanglement of multiple changes. To alleviate the problems, operation histories on IDEs' code editors are available as a new source of software evolution data nowadays. By replaying such histories, we can investigate past code changes in a fine-grained level. However, the prior studies did not provide enough evidence of their effectiveness for detecting refactoring transformations. This paper describes an experiment in which participants detect refactoring transformations performed by other participants after investigating the code changes with an operation-replay tool and diff tools. The results show that both approaches have their respective factors that pose misunderstanding and overlooking of refactoring transformations. Two negative factors on divided operations and generated compound operations were observed in the operation-based approach, whereas all the negative factors resulted from three problems on tangling, shadowing, and out-of-order of code changes in the difference-based approach. This paper also shows seven concrete examples of participants' mistakes in both approaches. These findings give us hints for improving existing tools for understanding code changes and detecting refactoring transformations.

关键词： software evolution fine-grained code change understanding code change refactoring detection

来源：评论

学校读者我要写书评

暂无评论

Vector Abstraction and Concretization for Scalable detection of refactorings 22

Vector Abstraction and Concretization for Scalable Detection...

引用

22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE)

作者： Milea, Narcisa Andreea Jiang, Lingxiao Khoo, Siau-Cheng Natl Univ Singapore Sch Comp Singapore Singapore Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450330565

Automated techniques have been proposed to either identify refactoring opportunities (i.e., code fragments that can be but have not yet been restructured in a program), or reconstruct historical refactorings (i.e., code restructuring operations that have happened between different versions of a program). In this paper, we propose a new technique that can detect both refactoring opportunities and historical refactorings in large code bases. The key of our technique is the design of vector abstraction and concretization operations that can encode code changes induced by certain refactorings as characteristic vectors. Thus, the problem of identifying refactorings can be reduced to the problem of identifying matching vectors, which can be solved efficiently. We have implemented our technique for Java. The prototype is applied to 200 bundle projects from the Eclipse ecosystem containing 4.5 million lines of code, and reports in total more than 32K instances of 17 types of refactoring opportunities, taking 25 minutes on average for each type. The prototype is also applied to 14 versions of 3 smaller programs (JMeter, Ant, XML-Security), and detects (1) more than 2.8K refactoring opportunities within individual versions with a precision of about 87%, and (2) more than 190 historical refactorings across consecutive versions of the programs with a precision of about 92%.

关键词： refactoring detection Software Evolution Vector-Based Code Representation

来源：评论

学校读者我要写书评

暂无评论

An Initial Investigation into Change-Based Reconstruction of Floss-refactorings

An Initial Investigation into Change-Based Reconstruction of...

引用

29th IEEE International Conference on Software Maintenance (ICSM)

作者： Soetens, Quinten David Perez, Javier Demeyer, Serge Univ Antwerp B-2020 Antwerp Belgium

Today, it is widely accepted that if refactoring is applied in practice, it is mainly interweaved with normal software development - so called "floss refactoring". Unfortunately, the current state-of-the-art is poorly equipped to mine floss refactoring from version histories, mainly because they infer refactorings by comparing two snapshots of a system and making educated guesses about the precise edit operations applied in between. In this paper we propose a solution that reconstructs refactorings not on snapshots of a system but using the actual changes as they are performed in an integrated development environment. We compare our solution against RefFinder and demonstrate that on a small yet representative program (the well-known "VideoRental system") our approach is more accurate in identifying occurrences of the "MOVEMETHOD" and "RENAMEMETHOD" refactorings.

关键词： First Class Changes Floss refactoring refactoring 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：