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Multi-modal program inference: a marriage of pre-trained language models and component-based synthesis

Authors:

Arjun Radhakrishna,

Gustavo Soares,

Ashish TiwariAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 5, Issue OOPSLA

Article No.: 158, Pages 1 - 29

https://doi.org/10.1145/3485535

Published: 15 October 2021 Publication History

Abstract

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. Program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification. We use our combination approach to instantiate multi-modal synthesis systems for two programming domains: the domain of regular expressions and the domain of CSS selectors. Our evaluation demonstrates the effectiveness of our domain-agnostic approach in comparison to a state-of-the-art specialized system, and the generality of our approach in providing multi-modal program synthesis from natural language and examples in different programming domains.

Supplementary Material

Auxiliary Presentation Video (oopsla21main-p410-p-video.mp4)

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. Program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification.

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Zhang JCambronero JGulwani SLe VPiskac RSoares GVerbruggen G(2024)PyDex: Repairing Bugs in Introductory Python Assignments using LLMsProceedings of the ACM on Programming Languages10.1145/36498508:OOPSLA1(1100-1124)Online publication date: 29-Apr-2024
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Helmuth TFedoroff JPantridge ESpector LLi XHandl J(2024)Facilitating Function Application in Code Building Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654068(887-895)Online publication date: 14-Jul-2024
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Patton NRahmani KMissula MBiswas JDillig I(2024)Programming-by-Demonstration for Long-Horizon Robot TasksProceedings of the ACM on Programming Languages10.1145/36328608:POPL(512-545)Online publication date: 5-Jan-2024
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Index Terms

Multi-modal program inference: a marriage of pre-trained language models and component-based synthesis
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Automatic programming
2. Theory of computation
  1. Semantics and reasoning
    1. Program constructs
    2. Program reasoning
      1. Program analysis

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 5, Issue OOPSLA

October 2021

2001 pages

EISSN:2475-1421

DOI:10.1145/3492349

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Published: 15 October 2021

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