Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing

• URL: http://arxiv.org/abs/2305.18584v2
• Date: Sun, 28 Apr 2024 17:45:56 GMT
• Title: Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing
• Authors: Jiayi Wei, Greg Durrett, Isil Dillig,
• Abstract summary: 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.
• Score: 57.776971051512234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developers often dedicate significant time to maintaining and refactoring existing code. However, most prior work on generative models for code focuses solely on creating new code, overlooking the distinctive needs of editing existing code. 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 codebase. Our model, Coeditor, is a fine-tuned language model specifically designed for code editing tasks. We represent code changes using a line diff format and employ static analysis to form large customized model contexts, ensuring the availability of appropriate information for prediction. We collect a code editing dataset from the commit histories of 1650 open-source Python projects for training and evaluation. In a simplified single-round, single-edit task, Coeditor significantly outperforms GPT-3.5 and SOTA open-source code completion models (bringing exact-match accuracy from 34.7 up to 60.4), demonstrating the benefits of incorporating editing history for code completion. In a multi-round, multi-edit setting, we observe substantial gains by iteratively conditioning on additional user edits. We have open-sourced our code, data, and model weights to encourage future research and have released a VSCode extension powered by our model for interactive IDE usage.

Related papers

• CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature [15.209899925736751]
  We propose CoEdPilot, an LLM-driven solution to recommend code edits by discriminating the relevant edits. CoEdPilot orchestrates multiple neural transformers to identify what and how to edit in the project regarding both edit location and edit content. Our experiments show that CoEdPilot can well predict the edits (i.e., predicting edit location with an accuracy of 70.8%-85.3%, and the edit content with an exact match rate of 41.8% and BLEU4 score of 60.7).
  arXiv  Detail & Related papers  (2024-08-03T10:23:05Z)
• CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [49.387195629660994]
  Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks. We curate diverse coding challenges and scenarios from five sources, covering various programming languages, complexity levels, and editing tasks.
  arXiv  Detail & Related papers  (2024-04-04T15:49:49Z)
• Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions [6.367360745627828]
  We introduce a benchmark of code editing tasks and use it to evaluate several cutting edge LLMs. Our evaluation exposes a significant gap between the capabilities of state-of-the-art open and closed models. We introduce a new, carefully curated, permissively licensed training dataset of code editing tasks coupled with natural language instructions.
  arXiv  Detail & Related papers  (2023-12-11T02:27:45Z)
• InstructCoder: Instruction Tuning Large Language Models for Code Editing [26.160498475809266]
  We explore the use of Large Language Models (LLMs) to edit code based on user instructions. InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits.
  arXiv  Detail & Related papers  (2023-10-31T10:15:35Z)
• Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors [53.819805242367345]
  We propose GRACE, a lifelong model editing method, which implements spot-fixes on streaming errors of a deployed model. GRACE writes new mappings into a pre-trained model's latent space, creating a discrete, local codebook of edits without altering model weights. Our experiments on T5, BERT, and GPT models show GRACE's state-of-the-art performance in making and retaining edits, while generalizing to unseen inputs.
  arXiv  Detail & Related papers  (2022-11-20T17:18:22Z)
• CodeEditor: Learning to Edit Source Code with Pre-trained Models [47.736781998792]
  This paper presents an effective pre-trained code editing model named CodeEditor. We collect lots of real-world code snippets as the ground truth and use a powerful generator to rewrite them into mutated versions. We conduct experiments on four code editing datasets and evaluate the pre-trained CodeEditor in three settings.
  arXiv  Detail & Related papers  (2022-10-31T03:26:33Z)
• InCoder: A Generative Model for Code Infilling and Synthesis [88.46061996766348]
  We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) and editing (via infilling) InCoder is trained to generate code files from a large corpus of permissively licensed code. Our model is the first generative model that is able to directly perform zero-shot code infilling.
  arXiv  Detail & Related papers  (2022-04-12T16:25:26Z)
• 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)
• Learning Structural Edits via Incremental Tree Transformations [102.64394890816178]
  We present a generic model for incremental editing of structured data (i.e., "structural edits") Our editor learns to iteratively generate tree edits (e.g., deleting or adding a subtree) and applies them to the partially edited data. We evaluate our proposed editor on two source code edit datasets, where results show that, with the proposed edit encoder, our editor significantly improves accuracy over previous approaches.
  arXiv  Detail & Related papers  (2021-01-28T16:11:32Z)

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.