Interactive Program Synthesis
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Abstract (arXiv)
Program synthesis from incomplete specifications (e.g. input-output examples) has gained popularity and found real-world applications, primarily due to its ease-of-use. Since this technology is often used in an interactive setting, efficiency and correctness are often the key user expectations from a system based on such technologies. Ensuring efficiency is challenging since the highly combinatorial nature of program synthesis algorithms does not fit in a 1-2 second response expectation of a user-facing system. Meeting correctness expectations is also difficult, given that the specifications provided are incomplete, and that the users of such systems are typically non-programmers. In this paper, we describe how interactivity can be leveraged to develop efficient synthesis algorithms, as well as to decrease the cognitive burden that a user endures trying to ensure that the system produces the desired program. We build a formal model of user interaction along three dimensions: incremental algorithm, step-based problem formulation, and feedback-based intent refinement. We then illustrate the effectiveness of each of these forms of interactivity with respect to synthesis performance and correctness on a set of real-world case studies.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2017.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 2.
6 papers.
- 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 119
- 2022Interactive Code Generation via Test-Driven User-Intent Formalizationcited by 50
- 2025Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMscited by 47
- 2025NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modificationcited by 3
- 2022Neural Program Synthesis with Querycited by 2
- 2022Example-based Synthesis of Static Analysis Rulescited by 0
- 2025NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification5 Aug 2025
- 2025Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs26 Feb 2025
- 2023Hypothesis Search: Inductive Reasoning with Language Models11 Sep 2023
- 2022Interactive Code Generation via Test-Driven User-Intent Formalization11 Aug 2022
- 2022Neural Program Synthesis with Query8 May 2022
- 2022Example-based Synthesis of Static Analysis Rules19 Apr 2022