Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History

• URL: http://arxiv.org/abs/2508.10074v2
• Date: Sun, 14 Sep 2025 15:44:13 GMT
• Title: Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
• Abstract summary: Next Edit Prediction is a novel task designed to infer developer intent from recent interaction history to predict the location and content of the subsequent edit.<n>This work lays the foundation for a new interaction paradigm that proactively collaborate with developers by anticipating their following action, rather than merely reacting to explicit instructions.
• Score: 34.91177430338037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low-latency code completion offers completion suggestions but is fundamentally constrained to the cursor's current position. On the other hand, chat-based editing can perform complex modifications, yet forces developers to stop their work, describe the intent in natural language, which causes a context-switch away from the code. This creates a suboptimal user experience, as neither paradigm proactively predicts the developer's next edit in a sequence of related edits. To bridge this gap and provide the seamless code edit suggestion, we introduce the task of Next Edit Prediction, a novel task designed to infer developer intent from recent interaction history to predict both the location and content of the subsequent edit. Specifically, we curate a high-quality supervised fine-tuning dataset and an evaluation benchmark for the Next Edit Prediction task. Then, we conduct supervised fine-tuning on a series of models and performed a comprehensive evaluation of both the fine-tuned models and other baseline models, yielding several novel findings. This work lays the foundation for a new interaction paradigm that proactively collaborate with developers by anticipating their following action, rather than merely reacting to explicit instructions. The code is available at https://github.com/lurf21/NextEditPrediction.

Related papers

• Learning Project-wise Subsequent Code Edits via Interleaving Neural-based Induction and Tool-based Deduction [18.337780894895623]
  We propose TRACE, a novel subsequent code editing solution to push the boundary of scope, accuracy, and efficiency.<n>TRACE predicts subsequent edits by interleaving neural-based induction for semantic edit prediction and tool-based deduction for syntactic edit prediction.
  arXiv  Detail & Related papers  (2026-04-14T02:56:21Z)
• CoEditor++: Instruction-based Visual Editing via Cognitive Reasoning [98.98349220451216]
  CoEditor++ is a training-free framework that decomposes editing into "what to edit" and "how to edit"<n>We show that CoEditor++ achieves state-of-the-art performance in both general editing and responsible editing tasks.<n>Our findings suggest the potential toward cognitive-centric instruction-based image editing.
  arXiv  Detail & Related papers  (2026-01-31T12:20:46Z)
• 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)
• 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)
• GrACE: Generation using Associated Code Edits [23.643567386291988]
  We endowing pre-trained large language models (LLMs) of code with the knowledge of prior, relevant edits. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits. We evaluate two well-known LLMs, Codex and CodeT5, in zero-shot and fine-tuning settings respectively.
  arXiv  Detail & Related papers  (2023-05-23T14:55:44Z)
• Using Developer Discussions to Guide Fixing Bugs in Software [51.00904399653609]
  We propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for additional information from developers. We demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.
  arXiv  Detail & Related papers  (2022-11-11T16:37:33Z)
• NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation [55.17069935305069]
  We introduce NeuroCounterfactuals, designed as loose counterfactuals, allowing for larger edits which result in naturalistic generations containing linguistic diversity. Our novel generative approach bridges the benefits of constrained decoding, with those of language model adaptation for sentiment steering.
  arXiv  Detail & Related papers  (2022-10-22T06:29:21Z)
• EditEval: An Instruction-Based Benchmark for Text Improvements [73.5918084416016]
  This work presents EditEval: An instruction-based, benchmark and evaluation suite for automatic evaluation of editing capabilities. We evaluate several pre-trained models, which shows that InstructGPT and PEER perform the best, but that most baselines fall below the supervised SOTA. Our analysis shows that commonly used metrics for editing tasks do not always correlate well, and that optimization for prompts with the highest performance does not necessarily entail the strongest robustness to different models.
  arXiv  Detail & Related papers  (2022-09-27T12:26:05Z)
• CoditT5: Pretraining for Source Code and Natural Language Editing [34.77621217370665]
  CoditT5 is a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments. We fine-tune it on various downstream editing tasks, including comment updating, bug fixing, and automated code review.
  arXiv  Detail & Related papers  (2022-08-10T16:59:40Z)
• Memory-Based Model Editing at Scale [102.28475739907498]
  Existing model editors struggle to accurately model an edit's intended scope. We propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC) SERAC stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed.
  arXiv  Detail & Related papers  (2022-06-13T23:40:34Z)
• A Structural Model for Contextual Code Changes [20.185486717922615]
  Given a code snippet that is partially edited, our goal is to predict a completion of the edit for the rest of the snippet. Our model achieves a 28% relative gain over state-of-the-art sequential models and 2x higher accuracy than syntactic models that learn to generate the edited code.
  arXiv  Detail & Related papers  (2020-05-27T07:16:19Z)

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.