Code Clone Refactoring in C# with Lambda Expressions

• URL: http://arxiv.org/abs/2512.21511v1
• Date: Thu, 25 Dec 2025 05:14:49 GMT
• Title: Code Clone Refactoring in C# with Lambda Expressions
• Authors: Takuto Kawamoto, Yoshiki Higo,
• Abstract summary: "Extract Method" is a technique for consolidating code clones.<n>Lambda expressions can be used to parameterize behaviors, but the specifications of each programming language significantly affect the applicability of this technique.<n>In this study, we propose a C#-specific technique that uses expressions to analyze code clones.
• Score: 0.49416305961918044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: "Extract Method" refactoring is a technique for consolidating code clones. Parameterization approaches are used to extract a single method from multiple code clones that contain differences. This approach parameterizes expressions and behaviors within a method. In particular, behavior parameterization has been extensively studied in Java programs, but little research has been conducted on other programming languages. Lambda expressions can be used to parameterize behaviors, but the specifications of each programming language significantly affect the applicability of this technique. Therefore, the optimal "Extract Method" approach may vary depending on the programming language. In this study, we propose a C#-specific technique that uses lambda expressions to analyze and consolidate code clones. We evaluated our proposed method by applying it to code clones detected by the NiCad clone detector and measuring how many of them could be successfully consolidated. In total, 2,217 clone pairs from 22 projects were included in our evaluation. For the clone pairs determined to be refactorable, we also attempted refactoring actually. The proposed approach determined that 35.0% of all clone pairs were suitable for refactoring. Among these, 28.9% were successfully refactored.

Related papers

• CodeTaste: Can LLMs Generate Human-Level Code Refactorings? [2.447746234944228]
  Large language model (LLM) coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt.<n>Human developers address such issues through: behavior-preserving program that improve structure and maintainability.<n>We present CodeTaste, a benchmark of tasks mined from large-scale multi-file changes in open-source repositories.
  arXiv  Detail & Related papers  (2026-03-04T15:34:18Z)
• Code Refactoring with LLM: A Comprehensive Evaluation With Few-Shot Settings [0.0]
  This study aims to develop a framework capable of performing accurate and efficient code across languages (C, C++, C#, Python, Java)<n>Java achieves the highest overall correctness up to 99.99% the 10-shot setting, records the highest average compilability of 94.78% compared to the original source code.
  arXiv  Detail & Related papers  (2025-11-26T14:47:52Z)
• HyClone: Bridging LLM Understanding and Dynamic Execution for Semantic Code Clone Detection [3.2167919219391474]
  Code clone detection is a critical task in software engineering, aimed at identifying duplicated or similar code fragments within or across software systems.<n>Recent advances in large language models (LLMs) have shown promise in understanding code semantics.<n>We propose a novel two-stage framework that combines LLM-based screening with execution-based validation for detecting semantic clones in Python programs.
  arXiv  Detail & Related papers  (2025-08-02T13:11:56Z)
• Recommending Variable Names for Extract Local Variable Refactorings [4.6621580809860514]
  VarNamer is an automated approach designed to recommend variable names for extract local variables.<n>VarNamer significantly increases the chance of exact match by 52.6% compared to Eclipse and 40.7% compared to IntelliJ IDEA.
  arXiv  Detail & Related papers  (2025-07-01T03:58:43Z)
• Rethinking Code Refinement: Learning to Judge Code Efficiency [60.04718679054704]
  Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. We propose a novel method based on the code language model that is trained to judge the efficiency between two different codes. We validate our method on multiple programming languages with multiple refinement steps, demonstrating that the proposed method can effectively distinguish between more and less efficient versions of code.
  arXiv  Detail & Related papers  (2024-10-29T06:17:37Z)
• AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection [69.79627042058048]
  AdaCCD is a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages.
  arXiv  Detail & Related papers  (2023-11-13T12:20:48Z)
• Opti Code Pro: A Heuristic Search-based Approach to Code Refactoring [0.0]
  The motivation for code could be to improve the design, structure, or implementation of an existing program without changing its functionality. To solve a very specific problem of coupling and cohesion, we propose using search-based techniques on an approximation of the full code problem.
  arXiv  Detail & Related papers  (2023-05-12T16:39:38Z)
• Supporting single responsibility through automated extract method refactoring [48.0838443679187]
  We propose a backward slicing algorithm to decompose a long method into slightly overlapping slices. The slices are computed for each output instruction, representing the outcome of a responsibility delegated to the method. On average, our experiments demonstrate at least a 29.6% improvement in precision and a 12.1% improvement in the recall of uncovering opportunities.
  arXiv  Detail & Related papers  (2023-05-05T11:02:02Z)
• Interactive Code Generation via Test-Driven User-Intent Formalization [60.90035204567797]
  Large language models (LLMs) produce code from informal natural language (NL) intent. It is hard to define a notion of correctness since natural language can be ambiguous and lacks a formal semantics. We describe a language-agnostic abstract algorithm and a concrete implementation TiCoder.
  arXiv  Detail & Related papers  (2022-08-11T17:41:08Z)
• ReACC: A Retrieval-Augmented Code Completion Framework [53.49707123661763]
  We propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval. We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
  arXiv  Detail & Related papers  (2022-03-15T08:25:08Z)
• Semantic Clone Detection via Probabilistic Software Modeling [69.43451204725324]
  This article contributes a semantic clone detection approach that detects clones that have 0% syntactic similarity. We present SCD-PSM as a stable and precise solution to semantic clone detection.
  arXiv  Detail & Related papers  (2020-08-11T17:54:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.