ChangePrism: Visualizing the Essence of Code Changes
- URL: http://arxiv.org/abs/2508.12649v2
- Date: Tue, 19 Aug 2025 13:50:27 GMT
- Title: ChangePrism: Visualizing the Essence of Code Changes
- Authors: Lei Chen, Michele Lanza, Shinpei Hayashi,
- Abstract summary: We present a novel visualization approach supported by a tool named ChangePrism.<n>The tool comprises two components: extraction, which retrieves code changes and relevant information from the git history, and visualization, which offers both general and detailed views of code changes in commits.<n>The general view provides an overview of different types of code changes across commits, while the detailed view displays the exact changes in the source code for each commit.
- Score: 9.321152185934105
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding the changes made by developers when they submit a pull request and/or perform a commit on a repository is a crucial activity in software maintenance and evolution. The common way to review changes relies on examining code diffs, where textual differences between two file versions are highlighted in red and green to indicate additions and deletions of lines. This can be cumbersome for developers, making it difficult to obtain a comprehensive overview of all changes in a commit. Moreover, certain types of code changes can be particularly significant and may warrant differentiation from standard modifications to enhance code comprehension. We present a novel visualization approach supported by a tool named ChangePrism, which provides a way to better understand code changes. The tool comprises two components: extraction, which retrieves code changes and relevant information from the git history, and visualization, which offers both general and detailed views of code changes in commits. The general view provides an overview of different types of code changes across commits, while the detailed view displays the exact changes in the source code for each commit.
Related papers
- Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation [21.625755841132733]
We propose an alternative way to condense code changes before generation.<n>We first condense code changes by using our proposed templates with the help of a tool named ChangeScribe.<n>Our approach can outperform six baselines in terms of BLEU-Norm, METEOR, and ROUGE-L.
arXiv Detail & Related papers (2025-09-19T04:04:28Z) - Altered Histories in Version Control System Repositories: Evidence from the Trenches [4.71599202491734]
We conduct the first-scale investigation of Git history alterations in public code repositories.<n>We find history alterations in 1.22 M repositories, for a total of 8.7 M rewritten histories.<n>We introduce GitHistorian, an automated tool that developers can use to spot and describe history alterations in public Git repositories.
arXiv Detail & Related papers (2025-09-11T09:34:06Z) - Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection [52.62459671461816]
This paper explores incorporating semantic priors from visual foundation models to improve the ability to detect changes.<n>Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features.
arXiv Detail & Related papers (2024-12-22T08:27:15Z) - Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
We analyzed 2,401 code review comments from Java open-source projects on GitHub.
83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
arXiv Detail & Related papers (2024-10-29T12:21:23Z) - ChangeGuard: Validating Code Changes via Pairwise Learning-Guided Execution [16.130469984234956]
ChangeGuard is an approach that uses learning-guided execution to compare the runtime behavior of a modified function.<n>Our results show that the approach identifies semantics-changing code changes with a precision of 77.1% and a recall of 69.5%.
arXiv Detail & Related papers (2024-10-21T15:13:32Z) - Understanding Code Change with Micro-Changes [9.321152185934105]
We present a catalog of micro-changes, together with an automated micro-change detector.
We found that our detector is capable of explaining more than 67% of the changes taking place in the systems under study.
arXiv Detail & Related papers (2024-09-16T01:47:25Z) - MS-Former: Memory-Supported Transformer for Weakly Supervised Change
Detection with Patch-Level Annotations [50.79913333804232]
We propose a memory-supported transformer (MS-Former) for weakly supervised change detection.
MS-Former consists of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS)
Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task.
arXiv Detail & Related papers (2023-11-16T09:57:29Z) - Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing [57.776971051512234]
In this work, we explore a multi-round code auto-editing setting, aiming to predict edits to a code region based on recent changes within the same.
Our model, Coeditor, is a fine-tuned language model specifically designed for code editing tasks.
In a simplified single-round, single-edit task, Coeditor significantly outperforms GPT-3.5 and SOTA open-source code completion models.
arXiv Detail & Related papers (2023-05-29T19:57:36Z) - Neighborhood Contrastive Transformer for Change Captioning [80.10836469177185]
We propose a neighborhood contrastive transformer to improve the model's perceiving ability for various changes under different scenes.
The proposed method achieves the state-of-the-art performance on three public datasets with different change scenarios.
arXiv Detail & Related papers (2023-03-06T14:39:54Z) - CCRep: Learning Code Change Representations via Pre-Trained Code Model
and Query Back [8.721077261941236]
This work proposes a novel Code Change Representation learning approach named CCRep.
CCRep learns to encode code changes as feature vectors for diverse downstream tasks.
We apply CCRep to three tasks: commit message generation, patch correctness assessment, and just-in-time defect prediction.
arXiv Detail & Related papers (2023-02-08T07:43:55Z) - Unsupervised Learning of General-Purpose Embeddings for Code Changes [6.652641137999891]
We propose an approach for obtaining embeddings of code changes during pre-training.
We evaluate them on two different downstream tasks - applying changes to code and commit message generation.
Our model outperforms the model that uses full edit sequences by 5.9 percentage points in accuracy.
arXiv Detail & Related papers (2021-06-03T19:08:53Z) - Deep Just-In-Time Inconsistency Detection Between Comments and Source
Code [51.00904399653609]
In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code.
We develop a deep-learning approach that learns to correlate a comment with code changes.
We show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system.
arXiv Detail & Related papers (2020-10-04T16:49:28Z)
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.