Machine programming-Reference-Cited by-同舟云学术

Machine programming

Published:2022-08 Issue:12 Volume:15 Page:3754-3757
ISSN:2150-8097
Container-title:Proceedings of the VLDB Endowment
language:en
Short-container-title:Proc. VLDB Endow.

Author:

Wasay Abdul¹,Tatbul Nesime²,Gottschlich Justin³

Affiliation:

1. Intel Labs

2. Intel Labs and MIT

3. Merly AI

Abstract

Machine programming is an emerging research area that improves the software development life cycle from design through deployment. We present a tutorial on machine programming research highlighting aspects relevant to the data systems community. We divide this tutorial into three parts: We begin with an introduction to machine programming introducing its three pillars: intention, invention, and adaptation. Then, we provide an overview of the data ecosystem central to all machine programming systems, highlighting challenges and novel opportunities relevant to the data systems community. Finally, we describe recent advances in machine programming research and how these directions use various data sets to improve the ease of creating and maintaining performant software systems.

Publisher

Association for Computing Machinery (ACM)

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Link

https://dl.acm.org/doi/pdf/10.14778/3554821.3554892

Reference40 articles.

1. Mejbah Alam Justin Gottschlich Nesime Tatbul Javier S Turek Tim Mattson and Abdullah Muzahid. 2019. A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. In Advances in Neural Information Processing Systems. Mejbah Alam Justin Gottschlich Nesime Tatbul Javier S Turek Tim Mattson and Abdullah Muzahid. 2019. A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. In Advances in Neural Information Processing Systems.

2. The adverse effects of code duplication in machine learning models of code

3. Uri Alon , Omer Levy , and Eran Yahav . 2019 . code2seq: Generating Sequences from Structured Representations of Code . In International Conference on Learning Representations. Uri Alon, Omer Levy, and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. In International Conference on Learning Representations.

4. Tal Ben-Nun , Alice Shoshana Jakobovits, and Torsten Hoefler . 2018 . Neural Code Comprehension: A Learnable Representation of Code Semantics . (2018). Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural Code Comprehension: A Learnable Representation of Code Semantics. (2018).

5. Mark Chen , Jerry Tworek , Heewoo Jun , Qiming Yuan , Henrique Ponde de Oliveira Pinto , Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 . Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021). Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021).