Synthesizing Object Models from Natural Language Specifications

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Gu, Alex

Advisors dc:contributor.advisor

• Solar-Lezama, Armando
• Andreas, Jacob

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144829

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144829