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Exploring the potential of general purpose LLMs in automated software refactoring: an empirical study

AUTOMATED software ENGINEERING 2025年第1期32卷 1-42页

作者： Liu, Bo Jiang, Yanjie Zhang, Yuxia Niu, Nan Li, Guangjie Liu, Hui Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Peking Univ Sch Comp Sci Key Lab High Confidence Software Technol Minist Educ Beijing 100871 Peoples R China Univ Cincinnati Dept Elect Engn & Comp Sci Cincinnati OH 45221 USA Natl Innovat Inst Def Technol Beijing 100071 Peoples R China

software refactoring is an essential activity for improving the readability, maintainability, and reusability of software projects. To this end, a large number of automated or semi-automated approaches/tools have been proposed to locate poorly designed code, recommend refactoring solutions, and conduct specified refactorings. However, even equipped with such tools, it remains challenging for developers to decide where and what kind of refactorings should be applied. Recent advances in deep learning techniques, especially in large language models (LLMs), make it potentially feasible to automatically refactor source code with LLMs. However, it remains unclear how well LLMs perform compared to human experts in conducting refactorings automatically and accurately. To fill this gap, in this paper, we conduct an empirical study to investigate the potential of LLMs in automated software refactoring, focusing on the identification of refactoring opportunities and the recommendation of refactoring solutions. We first construct a high-quality refactoring dataset comprising 180 real-world refactorings from 20 projects, and conduct the empirical study on the dataset. With the to-be-refactored Java documents as input, ChatGPT and Gemini identified only 28 and 7 respectively out of the 180 refactoring opportunities. The evaluation results suggested that the performance of LLMs in identifying refactoring opportunities is generally low and remains an open problem. However, explaining the expected refactoring subcategories and narrowing the search space in the prompts substantially increased the success rate of ChatGPT from 15.6 to 86.7%. Concerning the recommendation of refactoring solutions, ChatGPT recommended 176 refactoring solutions for the 180 refactorings, and 63.6% of the recommended solutions were comparable to (even better than) those constructed by human experts. However, 13 out of the 176 solutions suggested by ChatGPT and 9 out of the 137 solutions suggested by Gemini

关键词： software refactoring Large language model Empirical study software quality

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software refactoring at the function level using new Adaptive K-Nearest Neighbor algorithm

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ADVANCES IN ENGINEERING software 2010年第10-11期41卷 1160-1178页

作者： Alkhalid, Abdulaziz Alshayeb, Mohammad Mahmoud, Sabri King Fahd Univ Petr & Minerals Dept Informat & Comp Sci Dhahran 31261 Saudi Arabia 4700 King Abdullah Univ Sci & Technol Thuwal 239556900 Saudi Arabia

Improving the quality of software is a vital target of software engineering. Constantly evolving requirements force software developers to enhance, modify, or adapt software. Thus, increasing internal complexity, maintenance effort, and ultimately cost. In trying to balance between the needs to change software, maintain high quality, and keep the maintenance effort and cost low, refactoring comes up as a solution. refactoring aims to improve a number of quality factors, among which is understandability. Enhancing understandability of ill-structured software decreases the maintenance effort and cost. To improve understandability, designers need to maximize cohesion and minimize coupling, which becomes more difficult to achieve as software evolves and internal complexity increases. In this paper, we propose a new Adaptive K-Nearest Neighbor (A-KNN) algorithm to perform clustering with different attribute weights. The technique is used to assist software developers in refactoring at the function/method level. This is achieved by identifying ill-structured software entities and providing suggestions to improve cohesion of such entities. We also compare the proposed technique with three function-level clustering techniques Single Linkage algorithm (SLINK), Complete Linkage algorithm (CLINK) and Weighted Pair-Group Method using Arithmetic averages (WPGMA). A-KNN showed competitive performance with the other three algorithms and required less computational complexity. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： software refactoring Clustering Cohesion Code restructuring Function level cohesion software quality

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software refactoring at the package level using clustering techniques

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IET software 2011年第3期5卷 274-286页

作者： Alkhalid, A. Alshayeb, M. Mahmoud, A. King Abdullah Univ Sci & Technol Math & Comp Sci & Engn Div Thuwal 239556900 Saudi Arabia King Fahd Univ Petr & Minerals Dept Informat & Comp Sci Dhahran 31261 Saudi Arabia

Enhancing, modifying or adapting the software to new requirements increases the internal software complexity. software with high level of internal complexity is difficult to maintain. software refactoring reduces software complexity and hence decreases the maintenance effort. However, software refactoring becomes quite challenging task as the software evolves. The authors use clustering as a pattern recognition technique to assist in software refactoring activities at the package level. The approach presents a computer aided support for identifying ill-structured packages and provides suggestions for software designer to balance between intra-package cohesion and inter-package coupling. A comparative study is conducted applying three different clustering techniques on different software systems. In addition, the application of refactoring at the package level using an adaptive k-nearest neighbour (A-KNN) algorithm is introduced. The authors compared A-KNN technique with the other clustering techniques (viz. single linkage algorithm, complete linkage algorithm and weighted pair-group method using arithmetic averages). The new technique shows competitive performance with lower computational complexity.

关键词： intrapackage cohesion computer aided support software requirements software maintenance software metrics pattern recognition software design software evolution interpackage coupling software adaptation arithmetic average weighted pair-group method internal software complexity software engineering techniques software refactoring software metrics software modification complete linkage algorithm pattern clustering software enhancement software architecture clustering technique adaptive k-nearest neighbour algorithm single linkage algorithm computational complexity

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software refactoring for System Modernization

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IEEE software 2018年第6期35卷 62-67页

作者： Santos, Bruno M. Garcia-Rodriguez de Guzman, Ignacio de Camargo, Valter V. Piattini, Mario Ebert, Christof Univ Fed Sao Carlos Comp Sci Dept Sao Carlos SP Brazil Univ Castilla La Mancha Inst Technol & Informat Syst Ciudad Real Spain Univ Fed Sao Carlos AdvanSE Lab Adv Res Grp Software Engn Sao Carlos SP Brazil Univ Castilla La Mancha Alarcos Res Grp Ciudad Real Spain Vector Consulting Serv Stuttgart Germany Univ Stuttgart Stuttgart Germany Univ Sorbonne Paris France

Unlike their authors, software systems tend to live much longer than was ever intended or thought possible. Companies thus must modernize their software systems to keep them productive in new environments with new tec... 详细信息

关键词： software Maintenance software Tools System Modernization software refactoring refactoring Tools software Systems software Architecture software Development software Engineering Knowledge Discovery software Tools Architecture Driven Modernization ADM Legacy Systems Knowledge Discovery Metamodel KDM software Tools refactoring refactoring Tools software Engineering software Development software Technology

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Improving the Learning of Parallel Programming Using software refactoring

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2017年第1期25卷 112-119页

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

Parallel programming has become increasingly popular in the computer educational field over the past few years. Although parallel programs obtain the short execution time and the high throughput, learning how to write a well-structured and high-performance parallel program is still one of the challenges for most of students. How to let students learn parallel programming well is one of the important tasks that educators should resolve. This paper presents the learning of parallel programming using software refactoring methodologies and tools. Manual and automated refactoring are introduced to show how the learning is improved respectively. With manual refactoring, students learn how to perform the data or task decomposition and how to write a well-structured parallel software via customized programs and some benchmarks in JGF benchmark suite;with automated refactoring, students can transform the parallel parts quickly, and then evaluate the performance of a parallel software. Two automated refactoring tools are developed for educational purposes. Some of the experiences are also shared during conducting the course. (C) 2017 Wiley Periodicals, Inc.

关键词： parallel programming software refactoring manual refactoring automated refactoring tools learning

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Monitor-Based Instant software refactoring

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IEEE TRANSACTIONS ON software ENGINEERING 2013年第8期39卷 1112-1126页

作者： Liu, Hui Guo, Xue Shao, Weizhong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Peking Univ Key Lab High Confidence Software Technol Minist Educ Beijing 100871 Peoples R China Peking Univ Software Inst Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

software refactoring is an effective method for improvement of software quality while software external behavior remains unchanged. To facilitate software refactoring, a number of tools have been proposed for code smell detection and/or for automatic or semi-automatic refactoring. However, these tools are passive and human driven, thus making software refactoring dependent on developers' spontaneity. As a result, software engineers with little experience in software refactoring might miss a number of potential refactorings or may conduct refactorings later than expected. Few refactorings might result in poor software quality, and delayed refactorings may incur higher refactoring cost. To this end, we propose a monitor-based instant refactoring framework to drive inexperienced software engineers to conduct more refactorings promptly. Changes in the source code are instantly analyzed by a monitor running in the background. If these changes have the potential to introduce code smells, i.e., signs of potential problems in the code that might require refactorings, the monitor invokes corresponding smell detection tools and warns developers to resolve detected smells promptly. Feedback from developers, i.e., whether detected smells have been acknowledged and resolved, is consequently used to optimize smell detection algorithms. The proposed framework has been implemented, evaluated, and compared with the traditional human-driven refactoring tools. Evaluation results suggest that the proposed framework could drive inexperienced engineers to resolve more code smells (by an increase of 140 percent) promptly. The average lifespan of resolved smells was reduced by 92 percent. Results also suggest that the proposed framework could help developers to avoid similar code smells through timely warnings at the early stages of software development, thus reducing the total number of code smells by 51 percent.

关键词： software refactoring code smell detection monitor instant refactoring

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DEPICTER: A Design-Principle Guided and Heuristic-Rule Constrained software refactoring Approach

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IEEE TRANSACTIONS ON RELIABILITY 2022年第2期71卷 698-715页

作者： Zhao, Yangyang Yang, Yibiao Zhou, Yuming Ding, Zuohua Zhejiang Sci Tech Univ Hangzhou 314423 Peoples R China Nanjing Univ Nanjing 210023 Peoples R China

software refactoring is one of the most significant practices in software maintenance as the quality of software design tends to deteriorate during software evolution. But, refactoring software is a very challenging task as it requires a holistic view of the entire software system. To this end, recent studies introduced search-based algorithms to facilitate software refactoring. However, they still have the following major limitations: 1) the searched solutions may violate the design principles as their fitness functions do not directly reflect the degree of software's compliance with design principles;2) most approaches start the searching process from a completely random initial population, which may lead to unoptimal solutions. In this article, we aim to develop effective search-based refactoring approach to recommend better refactoring activities for developers which can improve the degree of software's compliance with design principles as well as the software design quality. We propose DEPICTER, a design-principle guided and heuristic-rule constrained software refactoring recommendation approach. In particular, DEPICTER uses non-dominated sorting genetic algorithm (NSGA)-II genetic algorithm and employs design-principle metrics as fitness functions. Besides, DEPICTER leverages heuristic rules to improve the quality of initial population for subsequent generic evolution. Our evaluations, based on four widely used systems, show that DEPICTER is effective for guiding the development of better refactoring models in practice.

关键词： software Statistics Sociology Measurement software algorithms Search problems Genetic algorithms Design principles heuristic rule search-based software maintenance software refactoring

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Automatic software refactoring via Weighted Clustering in Method-Level Networks

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IEEE TRANSACTIONS ON software ENGINEERING 2018年第3期44卷 202-236页

作者： Wang, Ying Yu, Hai Zhu, Zhiliang Zhang, Wei Zhao, Yuli Northeastern Univ Software Coll Shenyang 110819 Liaoning Peoples R China

In this study, we describe a system-level multiple refactoring algorithm, which can identify the move method, move field, and extract class refactoring opportunities automatically according to the principle of "high cohesion and low coupling." The algorithm works by merging and splitting related classes to obtain the optimal functionality distribution from the system-level. Furthermore, we present a weighted clustering algorithm for regrouping the entities in a system based on merged method-level networks. Using a series of preprocessing steps and preconditions, the "bad smells" introduced by cohesion and coupling problems can be removed from both the non-inheritance and inheritance hierarchies without changing the code behaviors. We rank the refactoring suggestions based on the anticipated benefits that they bring to the system. Based on comparisons with related research and assessing the refactoring results using quality metrics and empirical evaluation, we show that the proposed approach performs well in different systems and is beneficial from the perspective of the original developers. Finally, an open source tool is implemented to support the proposed approach.

关键词： Clustering analysis cohesion coupling complex network software refactoring

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The Effectiveness of Supervised Machine Learning Algorithms in Predicting software refactoring

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IEEE TRANSACTIONS ON software ENGINEERING 2022年第4期48卷 1432-1450页

作者： Aniche, Mauricio Maziero, Erick Durelli, Rafael Durelli, Vinicius H. S. Delft Univ Technol NL-2628 CD Delft Netherlands Univ Fed Lavras BR-37200900 Lavras MG Brazil Fed Univ Sao del Rei BR-36307352 Sao Joao Del Rei MG Brazil

refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers' expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90 percent. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts.

关键词： Biological system modeling Measurement Tools software Predictive models Context modeling Prediction algorithms software engineering software refactoring machine learning for software engineering

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A Brief Review on Multi-objective software refactoring and a New Method for Its Recommendation

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2021年第4期28卷 3087-3111页

作者： Kaur, Satnam Awasthi, Lalit K. Sangal, A. L. Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India

software refactoring is a commonly accepted means of improving the software quality without affecting its observable behaviour. It has gained significant attention from both academia and software industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization technique to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we provide a brief review on multi-objective search-based software refactoring and use a multi-objective spotted hyena optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results exhibit that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.

关键词： Search-based software engineering Code smell software refactoring Multi-objective optimization MOSHO algorithm software quality

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