MergeBERT: Program Merge Conflict Resolution via Neural Transformers

• URL: http://arxiv.org/abs/2109.00084v1
• Date: Tue, 31 Aug 2021 21:37:53 GMT
• Title: MergeBERT: Program Merge Conflict Resolution via Neural Transformers
• Authors: Alexey Svyatkovskiy, Todd Mytcowicz, Negar Ghorbani, Sarah Fakhoury, Elizabeth Dinella, Christian Bird, Neel Sundaresan, Shuvendu Lahiri
• Abstract summary: Merge conflicts can stall pull requests and continuous integration pipelines for hours to several days. We introduce MergeBERT, a novel neural program merge framework based on the token-level three-way differencing and a transformer model. Our model achieves 64--69% precision of merge resolution synthesis, yielding nearly a 2x performance improvement over existing structured and neural program merge tools.
• Score: 11.460182185916704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. In this paper, we introduce MergeBERT, a novel neural program merge framework based on the token-level three-way differencing and a transformer encoder model. Exploiting restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 64--69% precision of merge resolution synthesis, yielding nearly a 2x performance improvement over existing structured and neural program merge tools. Finally, we demonstrate versatility of our model, which is able to perform program merge in a multilingual setting with Java, JavaScript, TypeScript, and C# programming languages, generalizing zero-shot to unseen languages.

Related papers

• Evaluation of Version Control Merge Tools [3.1969855247377836]
  A version control system, such as Git, requires a way to integrate changes from different developers or branches. A merge tool either outputs a clean integration of the changes, or it outputs a conflict for manual resolution. New merge tools have been proposed, but they have not yet been evaluated against one another.
  arXiv  Detail & Related papers  (2024-10-13T17:35:14Z)
• CONGRA: Benchmarking Automatic Conflict Resolution [3.9910625211670485]
  ConGra is a benchmarking scheme designed to evaluate the performance of software merging tools. We build a large-scale evaluation dataset based on 44,948 conflicts from 34 real-world projects.
  arXiv  Detail & Related papers  (2024-09-21T12:21:41Z)
• WizardMerge -- Save Us From Merging Without Any Clues [8.21089093466603]
  We present WizardMerge, an auxiliary tool that leverages merging results from Git to retrieve code block dependency on text and LLVM-IR level. The outcomes demonstrate that WizardMerge diminishes conflict merging time costs, achieving a 23.85% reduction.
  arXiv  Detail & Related papers  (2024-07-03T05:40:29Z)
• Multi-Agent Software Development through Cross-Team Collaboration [30.88149502999973]
  We introduce Cross-Team Collaboration (CTC), a scalable multi-team framework for software development. CTC enables orchestrated teams to jointly propose various decisions and communicate with their insights. Results show a notable increase in quality compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-06-13T10:18:36Z)
• Token Fusion: Bridging the Gap between Token Pruning and Token Merging [71.84591084401458]
  Vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. computational overhead, largely attributed to the self-attention mechanism, makes deployment on resource-constrained edge devices challenging. We introduce "Token Fusion" (ToFu), a method that amalgamates the benefits of both token pruning and token merging.
  arXiv  Detail & Related papers  (2023-12-02T04:29:19Z)
• 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)
• An Empirical Study of Multimodal Model Merging [148.48412442848795]
  Model merging is a technique that fuses multiple models trained on different tasks to generate a multi-task solution. We conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture. We propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes.
  arXiv  Detail & Related papers  (2023-04-28T15:43:21Z)
• Speculative Decoding with Big Little Decoder [108.95187338417541]
  Big Little Decoder (BiLD) is a framework that can improve inference efficiency and latency for a wide range of text generation applications. On an NVIDIA T4 GPU, our framework achieves a speedup of up to 2.12x speedup with minimal generation quality degradation. Our framework is fully plug-and-play and can be applied without any modifications in the training process or model architecture.
  arXiv  Detail & Related papers  (2023-02-15T18:55:29Z)
• Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding [41.928263518867816]
  Conformer has proven to be effective in many speech processing tasks. Inspired by this, we propose a more flexible, interpretable and customizable encoder alternative, Branchformer.
  arXiv  Detail & Related papers  (2022-07-06T21:08:10Z)
• Cascaded Text Generation with Markov Transformers [122.76100449018061]
  Two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding with no output dependencies. This work proposes an autoregressive model with sub-linear parallel time generation. Noting that conditional random fields with bounded context can be decoded in parallel, we propose an efficient cascaded decoding approach for generating high-quality output. This approach requires only a small modification from standard autoregressive training, while showing competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
  arXiv  Detail & Related papers  (2020-06-01T17:52:15Z)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

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.