Software developers frequently use code completion tools to accelerate software development by suggesting the following code elements. Researchers usually employ AutoRegressive (AR) decoders to complete code sequences in a left-to-right, token-by-token fashion. To improve the accuracy and efficiency of code completion, we argue that tokens within a code statement have the potential to be predicted concurrently. In this article, we first conduct an empirical study to analyze the dependency among the target tokens in line-level code completion. The results suggest that it is potentially practical to generate all statement tokens in parallel. To this end, we introduce SANAR, a simple and effective syntax-aware non-autoregressive model for line-level code completion. To further improve the quality of the generated code, we propose an adaptive and syntax-aware sampling strategy to boost the model’s performance. The experimental results obtained from two widely used datasets indicate that our model outperforms state-of-the-art code completion approaches of similar model size by a considerable margin, and is faster than these models with up to 9× speed-up. Moreover, the extensive results additionally demonstrate that the enhancements achieved by SANAR become even more pronounced with larger model sizes, highlighting their significance.

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named Skcoderto mimic developers' code reuse behavior. Given a natural language requirement, Skcoderretrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models “how to write”. The post-editing further adds requirement-specific details into the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) Skcodercan generate more correct programs, and outperforms the state-of-the-art -CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@l. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our Skcoderin three aspects.

In programming, the names for the program entities, especially for the methods, are the intuitive characteristic for understanding the functionality of the code. To ensure the readability and maintainability of the programs, method names should be named properly. Specifically, the names should be meaningful and consistent with other names used in related contexts in their codebase. In recent years, many automated approaches are proposed to suggest consistent names for methods, among which neural machine translation (NMT) based models are widely used and have achieved state-of-the-art results. However, these NMT-based models mainly focus on extracting the code-specific features from the method body or the surrounding methods, the project-specific context and documentation of the target method are ignored. We conduct a statistical analysis to explore the relationship between the method names and their contexts. Based on the statistical results, we propose GTNM, a Global Transformer-based Neural Model for method name suggestion, which considers the local context, the project-specific context, and the documentation of the method simultaneously. Experimental results on java methods show that our model can outperform the state-of-the-art results by a large margin on method name suggestion, demonstrating the effectiveness of our proposed model.

Software extension is a fundamental challenge in software engineering which involves extending the functionalities of a software module without modifying it. Many modern software developers choose to adapt third-party extension platform to further improve customizability. As the project evolves, the requirements may change to include third-party extension support. However to design and to implement such platform is no trivial task, and should happen at the beginning of the project. In this paper, we have shown the four types of extensions that are often made to object-oriented software, namely Member Access Extension, Subclass Extension, Event-based Extension and Data Extension. And proposed a language-independent platform design that can be applied to an existing software project to support such third-party extensions. The platform exercises design patterns to implement its features. We also developed an Eclipse plugin that helps developers introduce the platform to existing Java software via semi-automatic code manipulation. We further conducted a comparative experiment to test our tool with volunteers from Waseda University and noticed a significant decrease of required effort.

Program comments have always been the key to understanding code. However, typical text comments can easily become verbose or evasive. Thus sometimes code reviewers find an audio or video code narration quite helpful. In this paper, we present our tool, called MCT (Multimedia Commenting Tool), which is an integrated development environment-based tool that enables programmers to easily explain their code by voice, video and mouse movement in the form of comments. With this tool, programmers can replay the audio or video when they feel like. A demonstration video can be accessed at: http://www.youtube.com/watch?v=tHEHqZme4VE.

Domain concepts and taxonomic relationships are an essential part of a domain ontology. They are used in a number of applications, including natural language processing, information retrieval, knowledge management and so on. Nowadays, with the continuous permeation of various kinds of Internet knowledge applications, numerous new concepts are emerged and released on to the Internet. So, the Internet has become an invaluable source of new concepts for almost every possible domain of knowledge. In order to ensure the domain ontologies keep pace with fast changing knowledge, we proposed an web searching based concepts and taxonomic relationships discovering approach. By our approach, the potential concepts on the Internet, which are taxonomically related with the give seeds concepts, can be discovered autonomously and iteratively. In this paper, the approach and a corresponding application in Chinese web pages are reported in detail. The experiments show that, our approach can catch the related domain concepts precisely, meanwhile, can reject irrelevant concepts and figure out the domain knowledge border definitely.