Developers often perform repetitive code editing activities for various reasons (e.g., code refactoring) during software development. Pre-trained code editing models have achieved the state-of-the-art (SOTA) results. Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task. Existing pre-training tasks mainly are code infilling tasks (e.g., masked language modeling), which are derived from the natural language processing field and are not designed for automatic code editing. This paper proposes a novel pre-training task specialized in code editing and presents an effective pre-trained code editing model named CodeEditor. Our pre-training task further improves the performance and generalization ability of code editing models. Specifically, we collect lots of real-world code snippets as the ground truth and use a powerful generator to rewrite them into mutated versions. Then, we pre-train our CodeEditor to edit mutated versions into the corresponding ground truth, to learn edit patterns. We conduct experiments on four code editing datasets and evaluate the pre-trained CodeEditor in three settings. (1) In the fine-tuning setting, we train the pre-trained CodeEditor with four datasets and evaluate it on the test data. CodeEditor outperforms the SOTA baselines by 15%, 25.5%, and 9.4% and 26.6% on four datasets. (2) In the few-shot setting, we train the pre-trained CodeEditor with limited data and evaluate it on the test data. CodeEditor substantially performs better than all baselines. (3) In the zero-shot setting, CodeEditor correctly edits 1,113 programs while the SOTA baselines can not work.

Tables are widely used with various structures to organize and present data. Recent attempts on table understanding mainly focus on relational tables, yet overlook to other common table structures. In this paper, we propose TUTA, a unified pre-training architecture for understanding generally structured tables. Since understanding a table needs to leverage both spatial, hierarchical, and semantic information, we adapt the self-attention strategy with several key structure-aware mechanisms. First, we propose a novel tree-based structure called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information in tables. Upon this, we extend the pre-training architecture with two core mechanisms, namely the tree-based attention and tree-based position embedding. Moreover, to capture table information in a progressive manner, we devise three pre-training objectives to enable representations at the token, cell, and table levels. TUTA pre-trains on a wide range of unlabeled tables and fine-tunes on a critical task in the field of table structure understanding, i.e. cell type classification. Experiment results show that TUTA is highly effective, achieving state-of-the-art on four well-annotated cell type classification datasets.

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.

--Code completion, one of the most useful features in the integrated development environments, can accelerate software development by suggesting the libraries, APIs, method names in real-time. Recent studies have shown that statistical language models can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from three major drawbacks: a) The hierarchical structural information of the programs is not fully utilized in the program's representation; b) In programs, the semantic relationships can be very long, existing LSTM based language models are not sufficient to model the long-term dependency. In this paper, we present a novel method that introduces the hierarchical structural information into the representation of programs by considering the path from the predicting node to the root node. T o capture the long-term dependency in the input programs, we apply Transformer-XL network as the base language model. Besides, we creatively propose a Multi-T ask Learning (MTL) framework to learn two related tasks in code completion jointly, where knowledge acquired from one task could be beneficial to another task. Experiments on three real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods. As the complexity and scale of the software developing continue to grow, code completion has become an essential feature of Integrated Development Environments (IDEs). It can speed up the process of software development by suggesting the next probable token based on existing code. However, traditional code completion tools rely on compile-time type information or heuristics rules to make recommendations [1], [2], which are costly and could not well capture human's programming patterns. To alleviate this problem, code completion research started to focus on learning from large-scale codebases in recent years. Based on the observation of source code's repeatability and predictability [3], statistical language models are generally used for modeling source code. N-gram is one of the most widely used language models [3]-[5]. Most recently, as the success of deep learning, source code modeling techniques have turned to Recurrent Neural Network (RNN) based models [2], [6]. In these models, a piece of source code is represented as source code token sequence or Abstract Syntactic Tree (AST) node sequence. Given a partial code sequence, the model computes the probability of the next token or AST node and recommends the one with the highest probability.