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Identification of move method refactoring Opportunities

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2009年第3期35卷 347-367页

作者： Tsantalis, Nikolaos Chatzigeorgiou, Alexander Univ Macedonia Dept Appl Math Thessaloniki 54006 Greece

Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of move method refactoring opportunities that constitute a way for solving many common Feature Envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel Entity Placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.

关键词： move method refactoring Feature Envy object-oriented design Jaccard distance design quality

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MMRUC3: A recommendation approach of move method refactoring using coupling, cohesion, and contextual similarity to enhance software design

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SOFTWARE-PRACTICE & EXPERIENCE 2018年第9期48卷 1560-1587页

作者： Rahman, Md. Masudur Riyadh, Rashed Rubby Khaled, Shah Mostafa Satter, Abdus Rahman, Md. Rayhanur Univ Dhaka Inst Informat Technol Dhaka Bangladesh

Placement of methods is one of the most important design activities for any object-oriented application in terms of coupling and cohesion. Due to method misplacement, the application becomes tightly coupled and loosely cohesive, reflecting inefficient design. Therefore, a feature envy code smell emerges from the application, as many methods use more features of other classes than its current class. Hence, development and maintenance time, cost, and effort are increased. To refactor the code smell and enhance the design quality, move method refactoring plays a significant role through grouping similar behaviors of methods. This is because the manual refactoring process is infeasible due to the necessity of huge time and most of the existing techniques consider only coupling-based and/or cohesion-based information of nonstatic entities (methods and attributes) for the recommendation. However, this article proposes an approach that uses contextual information, based on information retrieval techniques, along with dependency (coupling and cohesion)-based information of the application for the recommendation. In addition, the approach incorporates both static and nonstatic entities in the recommendation process. For validation, the approach is applied on seven well-known open source projects. The results of the experimental evaluation indicate that the proposed approach provides better results with an average precision of 18.91%, a recall of 69.91%, and an F-measure of 29.77% than the JDeodorant tool (a widely used eclipse plugin for refactorings). Moreover, this article establishes several relationships between the accuracy of the approach and project standards and sizes.

关键词： cohesion contextual similarity coupling feature envy code smell move method refactoring

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Predicting move method refactoring opportunities in object-oriented code

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INFORMATION AND SOFTWARE TECHNOLOGY 2017年 92卷 105-120页

作者： Al Dallal, Jehad Kuwait Univ Dept Informat Sci POB 5969 Safat 13060 Kuwait

Context: refactoring is the maintenance process of restructuring software source code to improve its quality without changing its external behavior. move method refactoring (MMR) refers to moving a method from one class to the class in which the method is used the most often. Manually inspecting and analyzing the source code of the system under consideration to determine the methods in need of MMR is a costly and time-consuming process. Existing techniques for identifying MMR opportunities have several limitations, such as scalability problems and being inapplicable in early development stages. Most of these techniques do not consider semantic relationships. Objective: We introduce a measure and a corresponding model to precisely predict whether a class includes methods in need of MMR. The measure is applicable once a class has entered the early development stages without waiting for other classes to be developed. method: The proposed measure considers both the cohesion and coupling aspects of methods. In addition, the measure uses structural and semantic data available within the class of interest. A statistical technique is applied to construct prediction models for classes that include methods in need of MMR. The models are applied on seven object-oriented systems to empirically evaluate their abilities to predict MMR opportunities. Results: The results show both that the prediction models based on the proposed measure had outstanding prediction abilities and that the measure was able to correctly detect more than 90% of the methods in need of MMR within the predicted classes. Conclusions: The proposed measure and corresponding prediction models are expected to greatly assist software engineers both in locating classes that include methods in need of MMR and in identifying these methods within the predicted classes. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Object-oriented design move method refactoring Class quality Logistic regression analysis

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Machine Learning-Based Exploration of the Impact of move method refactoring on Object-Oriented Software Quality Attributes

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第3期49卷 3867-3885页

作者： Al Dallal, Jehad Abdulsalam, Hanady AlMarzouq, Mohammad Selamat, Ali Gulf Univ Sci & Technol Dept Comp Sci Kuwait Kuwait Kuwait Univ Dept Informat Sci Safat Kuwait Kuwait Univ Dept Informat Syst & Operat Management Safat Kuwait Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Malaysia Univ Teknol Malaysia Media & Games Ctr Excellence MagicX Johor Baharu 81310 Malaysia

refactoring is a maintenance task that aims at enhancing the quality of a software's source code by restructuring it without affecting the external behavior. move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.

关键词： Class cohesion Class coupling Class size Internal quality attributes Logistic regression analysis Classification tree Machine learning move method refactoring Object-oriented software Quality measures Software metrics

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Three Heads Are Better Than One: Suggesting move method refactoring Opportunities with Inter-class Code Entity Dependency Enhanced Hybrid Hypergraph Neural Network 24

Three Heads Are Better Than One: Suggesting Move Method Refa...

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39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Cui, Di Wang, Jiaqi Wang, Qiangqiang Ji, Peng Qiao, Minglang Zhao, Yutong Hu, Jingzhao Wang, Luqiao Li, Qingshan Xidian Univ Xian Peoples R China Univ Cent Missouri Warrensburg MO USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

methods implemented in incorrect classes will cause excessive reliance on other classes than their own, known as a typical code smell symptom: feature envy, which makes it difficult to maintain increased coupling between classes. Addressing this issue, several move method refactoring tools have been proposed, employing a two-phase process: identifying misplaced methods to move and appropriate classes to receive, and implementing the mechanics of refactoring. These tools traditionally use hard-coded metrics to measure correlations between movable methods and target classes and apply heuristic thresholds or trained classifiers to unearth refactoring opportunities. Yet, these approaches predominantly illuminate pairwise correlations between methods and classes while overlooking the complex and complicated dependencies binding multiple code entities within these methods/classes that are prevalent in real-world cases. This narrow focus can lead to refactoring suggestions that may diverge from developers' actual needs. To bridge this gap, our paper leverages the concept of inter-class code entity dependency hypergraph to model complicated dependency relationships involving multiple code entities within various methods/classes and proposes a hypergraph learning-based approach to suggest move method refactoring opportunities named Hmove. We first construct inter-class code entity dependency hypergraphs from training samples and assign attributes to entities with a pre-trained code model. All the attributed hypergraphs are fed into a hybrid hypergraph neural network for training. Utilizing this trained neural network alongside a large language model, we construct a refactoring suggestion system. We trained Hmove on a large-scale dataset and evaluated it on two real-world datasets. The results show that demonstrates an increase of 27.8% in precision, 2.5% in recall, and 18.5% in f1-measure compared to 9 state-of-the-art refactoring tools, which is more useful for 68% of parti

关键词： move method refactoring Hypergraph Neural Network

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Evaluation of move method refactorings recommendation algorithms: are we doing it right? 3

Evaluation of Move Method refactorings recommendation algori...

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3rd IEEE/ACM International Workshop on refactoring (IWOR)

作者： Novozhilov, Evgenii Veselov, Ivan Pravilov, Mikhail Bryksin, Timofey St Petersburg State Univ St Petersburg Russia Higher Sch Econ JetBrains Res Moscow Russia St Petersburg State Univ JetBrains Res St Petersburg Russia

ISBN: (纸本)9781728122700

Previous studies introduced various techniques for detecting move method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for move method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for move method refactoring recommendation.

关键词： automatic refactoring recommendation move method refactoring feature envy algorithms evaluation code smells dataset generation

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move Code refactoring with Dynamic Analysis

Move Code Refactoring with Dynamic Analysis

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28th International Conference on Software Maintenance (ICSM)

作者： Kimura, Shuhei Higo, Yoshiki Igaki, Hiroshi Kusumoto, Shinji Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan

ISBN: (纸本)9781467323123

In order to reduce coupling and increase cohesion, we refactor program source code. Previous research efforts for suggesting candidates of such refactorings are based on static analysis, which obtains relations among classes or methods from source code. However, these approaches cannot obtain runtime information such as repetition count of loop, dynamic dispatch and actual execution path. Therefore, previous approaches might miss some refactoring opportunities. To tackle this problem, we propose a technique to find refactoring candidates by analyzing method traces. We have implemented a prototype tool based on the proposed technique and evaluated the technique on two software systems. As a result, we confirmed that the proposed technique could detect some refactoring candidates, which increase code quality.

关键词： refactoring dynamic analysis move method refactoring software maintenance

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To Improve Code Structure by Identifying move method Opportunities Using Frequent Usage Patterns in Source-Code 2nd

To Improve Code Structure by Identifying Move Method Opportu...

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2nd International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Singh, Randeep Kumar, Ashok Maharishi Markandeshwar Univ Dept Comp Sci & Engn Mullana Ambala 133207 Haryana India

ISBN: (纸本)9789811331404;9789811331398

A smelly code is generally an indication of the poor quality of the software and it increases the understandability and maintenance efforts at the software programmer's end. One technique to improve the quality is refactoring. Therefore, in this paper, we have identified the Feature Envy code smell and applied the corresponding move method refactoring. The code smell is tackled using the Frequent Usage Patterns (FUP's) present in the source-code of the software. The FUP's are identified at the method level and theyrepresent the set of member variables that are used by it either directly or indirectly. The identified FUP data is further used to cluster different methods using a newly proposed Clustering algorithm. Moreover, the proposed approach is successfully tested and evaluated on three standard open-source object-oriented software. The obtained results after evaluation confirm the ability of our proposed approach in enhancing the quality of the underlying software system.

关键词： Code smell Feature Envy move method refactoring Quality Cohesion Frequent usage patterns Hierarchical clustering

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