115 citations · 263 across the 14 of their papers we have counts for
7 papers · 1 filter
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…
TransRepair: Context-aware Program Repair for Compilation Errors
Xueyang Li, Shangqing Liu, Ruitao Feng +4
Automatically fixing compilation errors can greatly raise the productivity of software development, by guiding the novice or AI programmers to write and debug code. Recently, learn…
Learning Program Semantics with Code Representations: An Empirical Study
Jing Kai Siow, Shangqing Liu, Xiaofei Xie +2
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works pr…
A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps
Wei Wang, Guozhu Meng, Haoyu Wang +3
Authorship identification is the process of identifying and classifying authors through given codes. Authorship identification can be used in a wide range of software domains, e.g.…
Efficiently Manifesting Asynchronous Programming Errors in Android Apps
Lingling Fan, Ting Su, Sen Chen +4
Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital…
Large-Scale Analysis of Framework-Specific Exceptions in Android Apps
Lingling Fan, Ting Su, Sen Chen +5
Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to f…
AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis
Guozhu Meng, Yinxing Xue, Jing Kai Siow +3
Data driven research on Android has gained a great momentum these years. The abundance of data facilitates knowledge learning, however, also increases the difficulty of data prepro…