PSB2: The Second Program Synthesis Benchmark Suite
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Abstract (arXiv)
For the past six years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite to benchmark many aspects of automatic program synthesis systems. These problems have been used to make notable progress toward the goal of general program synthesis: automatically creating the types of software that human programmers code. Many of the systems that have attempted the problems in the original benchmark suite have used it to demonstrate performance improvements granted through new techniques. Over time, the suite has gradually become outdated, hindering the accurate measurement of further improvements. The field needs a new set of more difficult benchmark problems to move beyond what was previously possible. In this paper, we describe the 25 new general program synthesis benchmark problems that make up PSB2, a new benchmark suite. These problems are curated from a variety of sources, including programming katas and college courses. We selected these problems to be more difficult than those in the original suite, and give results using PushGP showing this increase in difficulty. These new problems give plenty of room for improvement, pointing the way for the next six or more years of general program synthesis research.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 3; 2026: 1.
9 papers.
- 2021Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programmingcited by 24
- 2023Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generationcited by 22
- 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 4
- 2023Particularitycited by 3
- 2025Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Modelscited by 3
- 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Reviewcited by 3
- 2022Functional Code Building Genetic Programmingcited by 2
- 2023Optimizing Neural Networks with Gradient Lexicase Selectioncited by 2
- 2025TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programmingcited by 2
- 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review25 Feb 2026
- 2025TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming14 Apr 2025
- 2025Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models10 Mar 2025
- 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality7 Mar 2025
- 2023Optimizing Neural Networks with Gradient Lexicase Selection19 Dec 2023
- 2023Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation27 Jun 2023
- 2023Particularity12 Jun 2023
- 2022Functional Code Building Genetic Programming9 Jun 2022
- 2021Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming15 Nov 2021