Example-Guided Synthesis of Relational Queries
Program synthesis tasks are commonly specified via input-output examples. Existing techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We present a new approach called example-guided synthesis, or EGS, which exploits data patterns in the examples to construct the desired program. We demonstrate EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.
Thu 21 OctDisplayed time zone: Central Time (US & Canada) change
10:50 - 12:10 | PLDI 2021, PLDI 2020, and OOPSLA 2020 Papers 1SIGPLAN Papers at Zurich G Chair(s): James Koppel Massachusetts Institute of Technology, USA | ||
10:50 15mTalk | Example-Guided Synthesis of Relational Queries SIGPLAN Papers Aalok Thakkar University of Pennsylvania, Aaditya Naik University of Pennsylvania, Nathaniel Sands University of Southern California, Mukund Raghothaman University of Southern California, Mayur Naik University of Pennsylvania, Rajeev Alur University of Pennsylvania | ||
11:05 15mTalk | Web Question Answering with Neurosymbolic Program Synthesis SIGPLAN Papers Jocelyn (Qiaochu) Chen University of Texas at Austin, USA, Aaron Lamoreaux University of Texas at Austin, Xinyu Wang University of Michigan, Greg Durrett University of Texas at Austin, USA, Osbert Bastani University of Pennsylvania, Işıl Dillig University of Texas at Austin | ||
11:20 15mTalk | Reactive Probabilistic Programming SIGPLAN Papers Guillaume Baudart IBM Research, USA, Louis Mandel IBM Research, Eric Atkinson Massachusetts Institute of Technology, Benjamin Sherman Massachusetts Institute of Technology, USA, Marc Pouzet École normale supérieure, Michael Carbin Massachusetts Institute of Technology DOI Pre-print | ||
11:35 15mTalk | A Sparse Iteration Space Transformation Framework for Sparse Tensor Algebra SIGPLAN Papers Ryan Senanayake Reservoir Labs, Changwan Hong Massachusetts Institute of Technology, Ziheng Wang Massachusetts Institute of Technology, Amalee Wilson Stanford University, Stephen Chou Massachusetts Institute of Technology, Shoaib Kamil Adobe Research, Saman Amarasinghe Massachusetts Institute of Technology, Fredrik Kjolstad Stanford University | ||
11:50 20mLive Q&A | Discussion, Questions and Answers SIGPLAN Papers | ||