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arXiv · 2605.03546

ProgramBench: Can Language Models Rebuild Programs From Scratch?

John Yang·Kilian Lieret·Jeffrey Ma·Parth Thakkar·Dmitrii Pedchenko·Sten Sootla·Emily McMilin·Pengcheng Yin·Rui Hou·Gabriel Synnaeve·Diyi Yang·Ofir Press
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Abstract

Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain, and grow codebases over extended periods with minimal human oversight. Such settings require models to make high-level software architecture decisions. However, existing benchmarks measure focused, limited tasks such as fixing a single bug or developing a single, specified feature. We therefore introduce ProgramBench to measure the ability of software engineering agents to develop software holisitically. In ProgramBench, given only a program and its documentation, agents must architect and implement a codebase that matches the reference executable's behavior. End-to-end behavioral tests are generated via agent-driven fuzzing, enabling evaluation without prescribing implementation structure. Our 200 tasks range from compact CLI tools to widely used software such as FFmpeg, SQLite, and the PHP interpreter. We evaluate 9 LMs and find that none fully resolve any task, with the best model passing 95\% of tests on only 3\% of tasks. Models favor monolithic, single-file implementations that diverge sharply from human-written code.

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John Yang, Kilian Lieret, Jeffrey Ma, Parth Thakkar, Dmitrii Pedchenko, Sten Sootla, Emily McMilin, Pengcheng Yin, Rui Hou, Gabriel Synnaeve, Diyi Yang, Ofir Press. 2026-05-05. ProgramBench: Can Language Models Rebuild Programs From Scratch?. https://arxiv.org/abs/2605.03546

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