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检索条件"主题词=Code refactoring"

共 87 条记录，以下是61-70 订阅

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Copilot-in-the-Loop: Fixing code Smells in Copilot-Generated Python code using Copilot 24

Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated...

引用

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

作者： Zhang, Beiqi Liang, Peng Feng, Qiong Fu, Yujia Li, Zengyang Wuhan Univ Sch Comp Sci Wuhan Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci Nanjing Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language Models have sparked growing interest in AI-enabled tools for both code generation and refactoring. GitHub Copilot is one such tool that has gained widespread usage. Copilot Chat, released in September 2023, functions as an interactive tool aimed at facilitating natural language-powered coding. However, limited attention has been given to understanding code smells in Copilot-generated Python code and Copilot Chat's ability to fix the code smells. To this end, we built a dataset comprising 102 code smells in Copilot-generated Python code. Our aim is to first explore the occurrence of code smells in Copilot-generated Python code and then evaluate the effectiveness of Copilot Chat in fixing these code smells employing different prompts. The results show that 8 out of 10 types of code smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one. For these code smells, Copilot Chat achieves a highest fixing rate of 87.1%, showing promise in fixing Python code smells generated by Copilot itself. In addition, the effectiveness of Copilot Chat in fixing these smells can be improved by providing more detailed prompts.

关键词： code Smell code Quality code refactoring GitHub Copilot

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An Exploratory Study on God Header Files in Open-Source C Projects 15

An Exploratory Study on God Header Files in Open-Source C Pr...

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15th Asia-Pacific Symposium on Internetware (Internetware)

作者： Wang, Yue Chang, Wenhui Zou, Yanzhen Xie, Bing Key Lab HCST PKU MOE Beijing Peoples R China Peking Univ SCS Beijing Peoples R China

ISBN: (纸本)9798400707056

God Header Files, like God Classes, pose difficulties in code understanding and lead to slow compilation in code evolution. However, there is little empirical evidence on their prevalence and impact. This study aims to investigate God Header Files in open-source C language software projects, in terms of their size, distribution, impact on compilation, and whether existing God Class refactoring approaches can decompose them effectively. We collected and quantitatively analyzed 557 popular open-source C language software projects and their commit histories, focusing on files with large code size, wide impact, and frequent modifications. Then we decomposed several typical header files using graph clustering algorithms and existing God Class refactoring methods. The most important results of our study can be summarized as follows: (1) 37.5% of the studied projects are affected by God Header Files, with 203 projects having 649 God Header Files;(2) 103 of the identified God Header Files have been modified hundreds of times, while 95% of the modifications only involve a small proportion of code. (3) Decomposing God Header Files with generic graph clustering algorithms and God Class refactoring methods could reduce recompilation to some extent during software evolution, but a new solution needs to be proposed in the future. Our study highlights the importance of God Header Files since they are widespread and can be harmful in software maintenance. Decomposition is an effective way to refactor God Header Files but better decomposing algorithms need to be proposed.

关键词： software maintenance code refactoring header file

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RAT: A refactoring-Aware Traceability Model for Bug Localization 23

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

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

作者： Niu, Feifei Assuncao, Wesley K. G. Huang, LiGuo Mayr-Dorn, Christoph Ge, Jidong Luo, Bin Egyed, Alexander Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Johannes Kepler Univ Linz Inst Software Syst Engn Linz Austria Southern Methodist Univ Dept Comp Sci & Engn Dallas TX USA

ISBN: (纸本)9781665457019

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

关键词： bug localization bug report similarity code refactoring traceability commit history information retrieval

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Improve the Performance of Mobile Applications Based on code Optimization Techniques Using PMD and Android Lint 5th

Improve the Performance of Mobile Applications Based on Code...

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5th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM)

作者： Nguyen, Man D. Huynh, Thang Q. Nguyen, T. Hung Duy Tan Univ Int Sch Da Nang Vietnam HUST Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9783319490465;9783319490458

Analyzing, testing, and optimizing source code are techniques that improve software quality and the performance of features and energy consumption of systems. Source code analysis includes analyzing the source code of an application and checking aspects to detect potential problems based on previous experience. In this paper, we investigate the rules and techniques of analyzing and optimizing Java source code by using PMD and Android lint. An automatic code-analyzing and code refactoring tool is developed with a set of rules based on the Eclipse refactoring API (plug-in) to getoptimized code that consumes less energy and improves performance for Android applications. The optimized code was tested in real environments with positive results. It reveals that programmer could use these techniques and support tool for developing Android applications with high quality source code and reliable and performance.

关键词： Static testing code analyzing Optimized code code refactoring Android testing PMD Android lint

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Evaluation of Vectorization Methods on Arm SVE Using the Exo Language

Evaluation of Vectorization Methods on Arm SVE Using the Exo...

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2024 International Conference on Cluster Computing

作者： Iwai, Rin Vatai, Emil Domke, Jens Sato, Yukinori Toyohashi Univ Technol Toyohashi Aichi Japan RIKEN R CCS Kobe Hyogo Japan

ISBN: (纸本)9798350383461;9798350383454

Vectorization is important for achieving high performance in numerical computations. This poster evaluates various vectorization methods for an application utilizing the Arm Scalable Vector Extensions (SVE) instructions. To fully control the vectorization "pipeline", we use the Exo language, which is a domain-specific language designed for performance engineers. In this poster, we discuss two existing vectorization strategies, known as Vector Length Agnostic (VLA) and Vector Length Specific (VLS) approaches. To evaluate the performance effects against these vectorized code, we generate them through the Exo compiler coupled with native backend compilers on the Fujitsu A64FX processor (i.e., gcc, clang, and fcc). Our results show that the code generated by the VLA and VLS approaches, including simple loop optimization, outperforms code without explicit vectorization. We also found that VLS often provides better performance over VLA. These results indicate that code transformation through "Exocompilation" is useful for optimizing code and investigating the behavior of compilers.

关键词： Auto-vectorization performance engineering code refactoring

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Iterative refactoring of Real-World Open-Source Programs with Large Language Models 16th

Iterative Refactoring of Real-World Open-Source Programs wit...

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16th Conference on Search-Based Software Engineering (SSBSE)

作者： Choi, Jinsu An, Gabin Yoo, Shin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783031645723;9783031645730

code refactoring is a critical task for improving software quality, but it is traditionally a manual, time-consuming process. This paper demonstrates an approach to automate project-level code refactoring using Large Language Models (LLMs). The key idea is to iteratively identify methods with high cyclomatic complexity, and then use LLMs to generate refactored implementations that reduce complexity. Our evaluation using 17 open-source projects shows that the proposed automated refactoring can reduce average cyclomatic complexity by up to 10.4% within 20 iterations. These results suggest that automated project-level code refactoring is feasible using LLMs with tailored prompts.

关键词： code refactoring Large Language Model Cyclomatic Complexity

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Deep Multimodal Architecture for Detection of Long Parameter List and Switch Statements using DistilBERT 22

Deep Multimodal Architecture for Detection of Long Parameter...

引用

IEEE 22nd International Working Conference on Source code Analysis and Manipulation (SCAM)

作者： Bhave, Anushka Sinha, Roopak Savitribai Phule Pune Univ Vishwakarma Inst Technol Comp Engn Pune Maharashtra India Auckland Univ Technol IT & Software Engn Auckland New Zealand

ISBN: (纸本)9781665496094

code smell detection and refactoring are crucial to sustain quality, reduce complexity and increase the efficiency of a software application. code smells are observable patterns in the source code of a program that indicate deeper structural issues. Most traditional methods for code smell classification rely exclusively on structural object-oriented metrics and manually-designed heuristics. We propose a novel multimodal deep learning approach that combines structural and semantic information to detect two commonly-encountered code smells: Long Parameter Lists and Switch Statements. The presented architecture applies transfer learning on DistilBERT to generate vector embeddings representing classes and methods concatenated with numerical metrics for joint feature extraction using CNN, to build a complex mapping between the features and predict the output as smelly or non-smelly. Subsequently, to perform a holistic comparative analysis we also implement two multimodal machine learning pipelines, the first employs a sci-kit learn TF-IDF Vectorizer with Random Forest Classifier, and the second merges CNN with Bi-LSTM. Our approach achieves an accuracy of 91.2% as corroborated by experimental evaluation, outperforming the state-of-the-art techniques.

关键词： code Smell Classification code refactoring Multimodal Deep Learning Long Parameter Lists Switch Statements

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A Structure-Driven Process of Automated refactoring to Design Patterns 38th

A Structure-Driven Process of Automated Refactoring to Desig...

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38th International Conference on Information Systems Architecture and Technology (ISAT)

作者： Derezinska, Anna Warsaw Univ Technol Inst Comp Sci Warsaw Poland

ISBN: (纸本)9783319672298;9783319672281

Design patterns can be introduced into an existing code by a code restructuring. It is counted to one of goals of code refactoring. This paper presents a process that automates this kind of refactoring. The approach is based on a structural code analysis aimed at design pattern relevance. The process consists of three main phases: code analysis, determination of a refactoring range, and realization of code restructuring. The latter is a complex code2code transformation, which comprises a series of code refactoring steps. A decision about a range of a design pattern application is taken by a user or can be automated. In both cases, it is supported by a software relevance metric. A framework for the whole process has been implemented as an extension of Eclipse. code refactoring to exemplary design patterns can be performed in a prototype tool for Java programs.

关键词： code refactoring Design patterns Eclipse

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Improving the Quality of Software by refactoring

Improving the Quality of Software by Refactoring

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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Kaur, Gurpreet Singh, Balraj Lovely Profess Univ Dept Comp Sci & Software Engn Phagwara India

ISBN: (纸本)9781538627457

Software code management has become another key skill required by software architects and software developers. Size of software increases with increase in count of features in software. code refactoring is process of reducing code maintenance cost. It is achieved by many different techniques like extract, move methods, fields or classes in code. In this research we focused on improving the maintainability of the code by looking into the different refactoring techniques and improving upon them. We proposed an algorithm to improve the refactoring process which results in higher maintainability. To look into the validity of our proposed algorithm, we have used Junit and reffinder to analyse the code and generate the result metrics. We have observed the effectiveness of our work by comparing the different code maintainability indexes generated by the tool. In our research we have examined four releases of the software project for code refactoring and maintainability. Adding some extra features and using enhanced refactoring techniques measuring the code metrics and comparing the results of current releases with the previous releases.

关键词： code refactoring bad smells refactoring process software metrics software quality attributes

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Profiling for Laziness 14

Profiling for Laziness

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41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL)

作者： Chang, Stephen Felleisen, Matthias Northeastern Univ PLT Boston MA 02115 USA

ISBN: (纸本)9781450325448

While many programmers appreciate the benefits of lazy programming at an abstract level, determining which parts of a concrete program to evaluate lazily poses a significant challenge for most of them. Over the past thirty years, experts have published numerous papers on the problem, but developing this level of expertise requires a significant amount of experience. We present a profiling-based technique that captures and automates this expertise for the insertion of laziness annotations into strict programs. To make this idea precise, we show how to equip a formal semantics with a metric that measures waste in an evaluation. Then we explain how to implement this metric as a dynamic profiling tool that suggests where to insert laziness into a program. Finally, we present evidence that our profiler's suggestions either match or improve on an expert's use of laziness in a range of real-world applications.

关键词： laziness profiling code refactoring

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