AI coding agents can modernize research software but can’t judge if the science is right

A field report from OpenAI and academic partners shows that coding agents can update and speed up aging research software. Much of the work, however, shifts from writing code to verifying the results.

Many widely used research tools began as supporting code for a single paper. Small academic teams often wrote them without the time or resources for proper testing, maintenance, or optimization. The result is fragile software that remains critical to entire fields but requires constant repairs. A field report from OpenAI and academic partners suggests AI coding agents could help close that gap. The report documents eight case studies, mostly in biology, in which research groups used coding agents such as Codex and Claude Code. The projects range from basic maintenance and targeted optimization to full rewrites in modern programming languages. Coding agents delivered speedups of more than 60 times One of the simpler projects involved modernizing cyvcf2, a Python library for reading genetic data. GPT-5.5 replaced its outdated build and installation process with a modern one. The MHCflurry migration was far more involved. MHCflurry is an immunology model that predicts which targets immune cells will recognize. Claude Code and Codex alternated between the roles of developer and reviewer while porting about 10,000 lines of code from TensorFlow to PyTorch. The rustar-aligner project was more ambitious. It rebuilt STAR from scratch in Rust. STAR maps sequencing reads from cells to the corresponding locations in a genome. The original contains more than 20,000 lines of C and C++ and is no longer actively maintained, even though it remains part of many research pipelines.