An Empirical Study on the Impact of Change Granularity in Refactoring Detection

• URL: http://arxiv.org/abs/2609.07482v1
• Date: Mon, 07 Sep 2026 13:35:58 GMT
• Title: An Empirical Study on the Impact of Change Granularity in Refactoring Detection
• Abstract summary: We call these types of changes (CGRs) and ephemerals (EPRs) as coarse-graineds (CGRs) and ephemerals (EPRs)<n>We investigated the features and causes of CGRs and EPRs through an empirical study of 32 open-source Java projects and found that both commonly occur during development.<n>In addition, we found that types related to splitting or merging classes and packages, as well as those involving modifications to the inheritance structure, tend to be CGRs, and types targeting small objects such as variables and attributes.
• Score: 5.134247368862501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting refactorings in commit history is essential to improve comprehension to code changes on code reviews, and to provide valuable information for empirical studies on software evolution. Techniques have been proposed to accurately detect refactorings on the granularity of a single commit. However, refactorings can be made over multiple commits because of their complexity or other practical development problems, which cause detecting on only the granularity of a single commit not enough. We observe that some refactorings can only be detected in coarser granularity, i.e., changes conducted over multiple commits, or in the granularity of a single commit but not in coarse-grained. We call these types of refactorings as coarse-grained refactorings (CGRs) and ephemeral refactorings (EPRs). We investigated the features and causes of CGRs and EPRs through an empirical study of 32 open-source Java projects and found that both commonly occur during development. In addition, we found that refactoring types related to splitting or merging classes and packages, as well as those involving modifications to the inheritance structure, tend to be CGRs, and types targeting small objects such as variables and attributes, and refactorings with context-sensitive detection criteria tend to be EPRs. The causes of CGRs and EPRs are analyzed and categorized, and the relationships between the commit messages of CGRs and themselves are also assessed. We found that about 20% of commit messages explicitly suggest the existence of CGRs. We suggest that CGRs and EPRs be valued in refactoring research and that detectors be extended to identify CGRs.

Related papers

• On the Influence of Refactoring Types on Merge Effort [70.15634443127651]
  We analyze 64 open-source Java projects and applied association rule mining to measure the strength of associations between specific types and merge effort.<n>Our results show that types relate to merge effort with varying strength.<n>Co-occurrences across parallel branches is associated with higher merge effort.
  arXiv  Detail & Related papers  (2026-08-15T19:28:01Z)
• 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)
• How do Agents Refactor: An Empirical Study [2.7711196026307476]
  We present the first analysis of agentic pull requests in Java.<n>We identify types and detect code smells before and after commits.<n>We find Cursor to be the only model to show a statistically significant increase in smells.
  arXiv  Detail & Related papers  (2026-01-28T01:34:15Z)
• Multi-Agent Coordinated Rename Refactoring [37.01164379102587]
  The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning.<n>Coordinated renaming, where a single rename triggers contexts in multiple, related identifiers, is a frequent yet challenging task.<n>We designed, implemented, and evaluated the first multi-agent framework that automates coordinated renaming.
  arXiv  Detail & Related papers  (2026-01-01T21:29:43Z)
• ZeroGR: A Generalizable and Scalable Framework for Zero-Shot Generative Retrieval [125.19156877994612]
  Generative retrieval (GR) reformulates information retrieval (IR) by framing it as the generation of document identifiers (docids)<n>We propose textscZeroGR, a zero-shot generative retrieval framework that leverages natural language instructions to extend GR across a wide range of IR tasks.<n>Specifically, textscZeroGR is composed of three key components: (i) an LM-based docid generator that unifies heterogeneous documents into semantically meaningful docids; (ii) an instruction-tuned query generator that generates diverse types of queries from natural language task descriptions to enhance
  arXiv  Detail & Related papers  (2025-10-12T03:04:24Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Refactoring $\neq$ Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis [54.361900378970134]
  Just-in-time defect prediction (JIT-DP) aims to predict the likelihood of code changes resulting in software defects at an early stage.<n>Prior research has largely ignored code during both the evaluation and methodology phases, despite its prevalence.<n>We propose Code chAnge Tactics (CAT) analysis to categorize code and its propagation, which improves labeling accuracy in the JIT-Defects4J dataset by 13.7%.
  arXiv  Detail & Related papers  (2025-07-25T23:29:25Z)
• When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements [56.29265568399648]
  We argue that disagreements prevent premature consensus and expand the explored solution space.<n>Disagreements on task-critical steps can derail collaboration depending on the topology of solution paths.
  arXiv  Detail & Related papers  (2025-02-21T02:24:43Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• State of Refactoring Adoption: Better Understanding Developer Perception of Refactoring [5.516979718589074]
  We aim to explore how developers document their activities during the software life cycle. We call such activity Self-Affirmed Refactoring (SAR), which indicates developers' documentation of their activities. We propose an approach to identify whether a commit describes developer-related events to classify them according to the common quality improvement categories.
  arXiv  Detail & Related papers  (2023-06-09T16:38:20Z)
• Do code refactorings influence the merge effort? [80.1936417993664]
  Multiple contributors frequently change the source code in parallel to implement new features, fix bugs, existing code, and make other changes. These simultaneous changes need to be merged into the same version of the source code. Studies show that 10 to 20 percent of all merge attempts result in conflicts, which require the manual developer's intervention to complete the process.
  arXiv  Detail & Related papers  (2023-05-10T13:24:59Z)
• How We Refactor and How We Document it? On the Use of Supervised Machine Learning Algorithms to Classify Refactoring Documentation [25.626914797750487]
  Refactoring is the art of improving the design of a system without altering its external behavior. This study categorizes commits into 3 categories, namely, Internal QA, External QA, and Code Smell Resolution, along with the traditional BugFix and Functional categories. To better understand our classification results, we analyzed commit messages to extract patterns that developers regularly use to describe their smells.
  arXiv  Detail & Related papers  (2020-10-26T20:33:17Z)

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.