Source Code Recommender Systems: The Practitioners' Perspective
The automatic generation of source code is one of the long-lasting dreams in software engineering research. Several techniques have been proposed to speed up the writing of new code. For example, code completion techniques can recommend to developers the next few tokens they are likely to type, while retrieval-based approaches can suggest code snippets relevant to the task at hand. Also, deep learning has been used to automatically generate code statements starting from a natural language description. While research in this field is very active, there is no study investigating what the users of code recommender systems (i.e., software practitioners) actually need from these tools. We present a study involving 80 software developers to investigate the characteristics of code recommender systems they consider important. The output of our study is a taxonomy of 70 “requirements” that should be considered when designing code recommender systems. For example, developers would like the recommended code to use the same coding style of the code under development. Also, code recommenders being “aware” of the developers’ knowledge (e.g., what are the frameworks/libraries they already used in the past) and able to customize the recommendations based on this knowledge would be appreciated by practitioners. The taxonomy output of our study points to a wide set of future research directions for code recommenders.
Fri 19 MayDisplayed 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 | ||