416 citations · 1k across the 12 of their papers we have counts for
3 papers · 1 filter
Exploring and Evaluating Personalized Models for Code Generation
Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy +3
Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling so…
DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons
Dawn Drain, Colin B. Clement, Guillermo Serrato +1
The joint task of bug localization and program repair is an integral part of the software development process. In this work we present DeepDebug, an approach to automated debugging…
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren +19
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine le…