Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Code summarization and generation empower conversion between programming language
(PL) and natural language (NL), while code translation avails the migration of legacy code …

Benchmark datasets have a significant impact on accelerating research in programming
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …

Pre-trained models for programming language have achieved dramatic empirical
improvements on a variety of code-related tasks such as code search, code completion …

Evaluation metrics play a vital role in the growth of an area as it defines the standard of
distinguishing between good and bad models. In the area of code synthesis, the commonly …

Pre-trained Generative Language models (eg, PLBART, CodeT5, SPT-Code) for source
code yielded strong results on several tasks in the past few years, including code generation …

Automated code generation can be a powerful technique for software development,
significantly reducing developers' efforts and time required to create new code by generating …

Recent advances in Named Entity Recognition (NER) show that document-level contexts
can significantly improve model performance. In many application scenarios, however, such …

Software developers write a lot of source code and documentation during software
development. Intrinsically, developers often recall parts of source code or code summaries …

Code Generation aims at generating relevant code fragments according to given natural
language descriptions. In the process of software development, there exist a large number of …