Synthesizing Object Models from Natural Language Specifications
Name
Gu-gua-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
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475.52 KB
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Adobe PDF
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Author(s)
Gu, Alex
Advisor(s)
Solar-Lezama, Armando
Andreas, Jacob
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Program synthesis has traditionally excelled in tasks with precise specifications such as input-output examples and formal constraints by using structured and algorithmic approaches based on enumerative search and type inference. However, traditional synthesis techniques have no mechanism of incorporating real-world knowledge, which is commonplace in software engineering. Motivated by this, we introduce a new synthesis task known as specification reification: synthesizing concrete realizations of vague, high-level application specifications. We focus on a specific instance of this: generating object models from natural language application descriptions. Towards this goal, we present three approaches for object model synthesis that leverage domain knowledge from the GPT-3 language model. In addition, we design a scoring metric to evaluate the success of synthesized object models on seven sample tasks such as classroom management and pet store applications. We demonstrate that our language-model-based synthesizers generate object models that are comparable in quality to human-generated ones.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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