Program Synthesis Using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We pr...

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Published in:Proceedings / International Conference on Software Engineering pp. 345 - 356
Main Authors: Desai, Aditya, Gulwani, Sumit, Hingorani, Vineet, Jain, Nidhi, Karkare, Amey, Marron, Mark, R., Sailesh, Roy, Subhajit
Format: Conference Proceeding
Language:English
Published: ACM 01.05.2016
Subjects:
Automata
Benchmark testing
Domain Specific Languages
DSL
End-user Programming
Natrual Language Programming
Natural languages
Program Synthesis
Synthesizers
Training
Training data
User Intent
ISSN:1558-1225
Online Access:Get full text
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Abstract Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.
AbstractList Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.
Author Hingorani, Vineet
Karkare, Amey
Marron, Mark
Jain, Nidhi
R., Sailesh
Desai, Aditya
Gulwani, Sumit
Roy, Subhajit
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  email: subhajit@cse.iitk.ac.in
  organization: IIT Kanpur
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Snippet Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off,...
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StartPage 345
SubjectTerms Automata
Benchmark testing
Domain Specific Languages
DSL
End-user Programming
Natrual Language Programming
Natural languages
Program Synthesis
Synthesizers
Training
Training data
User Intent
Title Program Synthesis Using Natural Language
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