Directional Diffusion-Style Code Editing Pre-Training

Code pre-trained models have shown promising effectiveness in various software engineering tasks. Among these tasks, many tasks are related to software evolution and/or code editing. However, existing code pre-trained models often overlook the real-world code editing data and the evolutionary nature of the editing process. In this paper, to simulate the step-by-step code editing process of human developers, we propose DivoT5, a pre-trained model based on directional diffusion at the data level. In DivoT5, we adopt two categories of pre-training tasks. The first category is mask and denoising tasks augmented with a diffusion direction representing code evolution. That is, we first apply a noising process to the code snippets before evolution, and then ask the pre-training process to restore the snippets with noise into the code snippets after evolution. The second category is tasks aiming to reinforce the evolutionary direction. That is, we first generate various intermediate versions for each pair of snippets before and after evolution, and then ask the pre-training process to transform the intermediate versions into the snippet after evolution for each pair. We evaluate DivoT5 for two code-editing scenarios (including a number of tasks) and one non-editing scenario using four downstream tasks. For each downstream task, we fine-tune the pre-trained DivoT5 on multiple corresponding datasets and evaluate its effectiveness across diverse scenarios Our experimental results show that ivoT5 achieves state-of-the-art (SOTA) performance on most tasks in comparison to models of the same scale (220M), large-scale (770M, 6.7B) models in fine-tuning, and billion-scale (6.7B, 8B, ChatGPT) instruct models in few-shot settings. For one code-editing task (i.e., CodeReview in NL-based CodeRefinement task), DivoT5 pre-trained on top of CodeT5-small (60M) can even outperform CodeT5-base (220M) and other pre-trained models with 220M parameters except for DivoT5 pre-trained on top of CodeT5-base (220M).