Write a Blog >>
ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Wed 23 Sep 2020 01:30 - 01:50 at Koala - Recommender Systems for Software Engineering Chair(s): Shaowei Wang

Code completion is one of the most useful features in the Integrated Development Environments (IDEs), which can accelerate software development by suggesting the next probable token based on the contextual code in real-time. Recent studies have shown that statistical language modeling techniques can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from two major drawbacks: a) Existing research uses static embeddings, which map a word to the same vector regardless of its context. The differences in the meaning of a token in varying contexts are lost when each token is associated with a single representation; b) Existing LM-based code completion models perform poor on completing identifiers, and the type information of the identifiers is ignored in most of these models. To address these challenges, in this paper, we develop a multi-task learning based pre-trained language model for code understanding and code generation with a Transformer-based neural architecture. We pre-train it with hybrid objective functions that incorporate both code understanding and code generation tasks. Then we fine-tune the pre-trained model on code completion. During the completion, our model does not directly predict the next token. Instead, we adopt multi-task learning to predict the token and its type jointly and utilize the predicted type to assist the token prediction. Experiments results on two real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods.

Wed 23 Sep
Times are displayed in time zone: (UTC) Coordinated Universal Time

01:10 - 02:10: Recommender Systems for Software EngineeringResearch Papers / Tool Demonstrations at Koala
Chair(s): Shaowei WangMississippi State University
01:10 - 01:30
API-Misuse Detection Driven by Fine-Grained API-Constraint Knowledge Graph
Research Papers
Xiaoxue RenZhejiang University, Xinyuan YeAustralian National University, Zhenchang XingAustralian National University, Australia, Xin XiaMonash University, Xiwei XuData61 at CSIRO, Australia, Liming ZhuData61 at CSIRO, Australia / UNSW, Australia, Jianling SunZhejiang University
01:30 - 01:50
Multi-task Learning based Pre-trained Language Model for Code Completion
Research Papers
Fang LiuPeking University, Ge LiPeking University, Yunfei ZhaoPeking University, Zhi JinPeking University
01:50 - 02:00
HomoTR: Online Test Recommendation System Based on Homologous Code Matching
Tool Demonstrations
Chenqian ZhuNanjing University, Weisong SunState Key Laboratory for Novel Software Technology, Nanjing University, Qin LIU, Yangyang YuanNanjing University, Chunrong FangNanjing University, China, Yong HuangState Key Laboratory for Novel Software Technology, Nanjing University