D-Diff: An Interactive Environment for Adjusting Commit Boundaries Based on an Editable 3-way Diff

• URL: http://arxiv.org/abs/2608.22207v1
• Date: Sun, 23 Aug 2026 04:00:47 GMT
• Title: D-Diff: An Interactive Environment for Adjusting Commit Boundaries Based on an Editable 3-way Diff
• Abstract summary: D-Diff is an interactive diff adjustment environment for two consecutive commits that each involve the same single file.<n>Based on a 3-way diff display, D-Diff integrates the two diffs into a single compact view, which also provides a way to modify them.
• Score: 0.254890465057467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In version control, it is recommended that each commit include only changes related to one task. To follow this recommendation, developers may need to adjust commit boundaries, that is, to compare and modify the diffs between two consecutive commits. Existing tools either display only a single diff at a time, forcing developers to rely on their memory when comparing diffs, or display three files simultaneously without showing the two consecutive diffs, forcing developers to infer them; both increase their cognitive load and hamper the adjustment. As a first step toward supporting this process, we propose D-Diff, an interactive diff adjustment environment for two consecutive commits that each involve the same single file. Based on a 3-way diff display, D-Diff integrates the two diffs into a single compact view, which also provides a way to modify them. A within-subjects user study with 8 participants showed that D-Diff significantly outperforms a baseline tool in terms of efficiency, reducing the median adjustment time from 530 seconds to 230 seconds (approximately 57%). D-Diff also received a higher percentage of positive responses across all usability questionnaire items, while no statistically significant difference was found in accuracy.

Related papers

• Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models [0.0]
  We train two code models in two output regimes: direct generation and diff-based generation.<n>Direct generation substantially outperforms diff-based generation on every metric we measure.<n>We identify a single, architecture-independent mechanism behind the conditions where diff-based generation does win.
  arXiv  Detail & Related papers  (2026-09-05T00:13:49Z)
• DICS: Exploring Data Intrinsic Consistency for Visual Instruction Selection [53.457992197673185]
  Data Intrinsic Consistency (DIC) is a self-scoring metric designed to quantify the sample-level inter-component consistency.<n>DICS is an adaptive data selection method that optimize the trade-off between high intra-sample consistency and global distributional diversity.<n>DIC-6M is a 6M-sample multi-modal instruction corpus that enables the largest-scale visual instruction selection study to date.
  arXiv  Detail & Related papers  (2026-08-31T03:44:54Z)
• HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning [7.256466895987238]
  HERO builds benchmark streams by separating three choices that are often coupled, namely the task split, the client data split, and the client task sequence.<n>We evaluate representative FCL methods on CIFAR-100 and TinyImageNet using final average accuracy, average forgetting, and bottom-10% client accuracy.
  arXiv  Detail & Related papers  (2026-06-13T10:32:34Z)
• To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing [54.399335188667045]
  BlockDiff and FuncDiff are structure-aware diff formats that represent changes as block-level rewrites of syntactically coherent units.<n>AdaEdit is a general adaptive edit strategy that trains LLMs to dynamically choose the most token-efficient format between a given diff format and full code.
  arXiv  Detail & Related papers  (2026-04-30T01:14:13Z)
• Diff-XYZ: A Benchmark for Evaluating Diff Understanding [38.94055952813874]
  We introduce Diff-XYZ, a compact benchmark for code-diff understanding with three supervised tasks.<n> Instances in the benchmark are triples $langle textitold code, textitnew code, textitdiff rangle$ drawn from real commits in CommitPackFT.
  arXiv  Detail & Related papers  (2025-10-14T13:23:01Z)
• Video Action Differencing [92.3218782696305]
  We introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences between videos of the same action.<n>We first create VidDiffBench, a benchmark dataset containing 549 video pairs.<n>Our experiments demonstrate that VidDiffBench poses a significant challenge for state-of-the-art large multimodal models.
  arXiv  Detail & Related papers  (2025-03-10T21:18:32Z)
• Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance [1.313675711285772]
  We propose an interactive approach to optimize source code differences (diffs) Users can provide feedback for the points of a diff that should not be matched but are or parts that should be matched but are not. The results of 23 GitHub projects confirm that 92% of nonoptimal diffs can be addressed with less than four feedback actions in the ideal case.
  arXiv  Detail & Related papers  (2024-09-20T15:43:55Z)
• Describing Differences in Image Sets with Natural Language [101.80939666230168]
  Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets. We introduce VisDiff, which first captions the images and prompts a language model to propose difference descriptions. We are able to find interesting and previously unknown differences in datasets and models, demonstrating VisDiff's utility in revealing nuanced insights.
  arXiv  Detail & Related papers  (2023-12-05T18:59:16Z)
• FeatAug-DETR: Enriching One-to-Many Matching for DETRs with Feature Augmentation [48.94488166162821]
  One-to-one matching is a crucial design in DETR-like object detection frameworks. We propose two methods that realize one-to-many matching from a different perspective of augmenting images or image features. We conduct extensive experiments to evaluate the effectiveness of the proposed approach on DETR variants.
  arXiv  Detail & Related papers  (2023-03-02T18:59:48Z)
• Parameter-Efficient Transfer Learning with Diff Pruning [108.03864629388404]
  diff pruning is a simple approach to enable parameter-efficient transfer learning within the pretrain-finetune framework. We find that models finetuned with diff pruning can match the performance of fully finetuned baselines on the GLUE benchmark.
  arXiv  Detail & Related papers  (2020-12-14T12:34:01Z)

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.