Programming by Example Made Easy

TL;DR

The paper introduces Bee, a user-friendly PBE framework that simplifies program synthesis across domains by using an entity-action model and bidirectional search, reducing the need for domain-specific languages or algorithms.

Contribution

Bee provides a novel, domain-agnostic PBE framework leveraging relational tables and bidirectional search, making PBE tools easier to develop and adopt without specialized expertise.

Findings

Bee outperforms existing frameworks in ease of use and learning curve.

Bidirectional search achieves performance comparable to specialized synthesizers.

Evaluation on diverse tasks demonstrates Bee’s broad applicability.

Abstract

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee's effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers.