While a large number of pre-trained models have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understanding of these pre-trained models is arguably fairly limited. With the goal of advancing our understanding of these models, we perform the first systematic empirical comparison of 22 recently-developed pre-trained models on 13 SE tasks. To gain additional insights into these models, we adopt a 4-dimensional categorization of pre-trained models, and subsequently investigate whether there are correlations between different categories of pre-trained models and their performances on different SE tasks.
Fri 19 MayDisplayed time zone: Hobart change
Fri 19 May
Displayed time zone: Hobart change
13:45 - 15:15 | Code generationJournal-First Papers / Technical Track at Meeting Room 101 Chair(s): Iftekhar Ahmed University of California at Irvine | ||
13:45 15mTalk | Learning Deep Semantics for Test Completion Technical Track Pengyu Nie University of Texas at Austin, Rahul Banerjee The University of Texas at Austin, Junyi Jessy Li University of Texas at Austin, USA, Raymond Mooney The University of Texas at Austin, Milos Gligoric University of Texas at Austin | ||
14:00 15mTalk | Dynamic Human-in-the-Loop Assertion Generation Journal-First Papers Lucas Zamprogno University of British Columbia, Braxton Hall University of British Columbia, Reid Holmes University of British Columbia, Joanne M. Atlee University of Waterloo | ||
14:15 15mTalk | SkCoder: A Sketch-based Approach for Automatic Code Generation Technical Track Jia Li Peking University, Yongmin Li Peking University, Ge Li Peking University, Zhi Jin Peking University, Xing Hu Zhejiang University Pre-print | ||
14:30 15mTalk | An Empirical Comparison of Pre-Trained Models of Source Code Technical Track Changan Niu Software Institute, Nanjing University, Chuanyi Li Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688, Dongxiao Chen Software Institute, Nanjing University, Jidong Ge Nanjing University, Bin Luo Nanjing University Pre-print | ||
14:45 15mTalk | On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot Technical Track Antonio Mastropaolo Università della Svizzera italiana, Luca Pascarella ETH Zurich, Emanuela Guglielmi University of Molise, Matteo Ciniselli Università della Svizzera Italiana, Simone Scalabrino University of Molise, Rocco Oliveto University of Molise, Gabriele Bavota Software Institute, USI Università della Svizzera italiana | ||
15:00 15mTalk | Source Code Recommender Systems: The Practitioners' Perspective Technical Track Matteo Ciniselli Università della Svizzera Italiana, Luca Pascarella ETH Zurich, Emad Aghajani Software Institute, USI Università della Svizzera italiana, Simone Scalabrino University of Molise, Rocco Oliveto University of Molise, Gabriele Bavota Software Institute, USI Università della Svizzera italiana | ||