Agentic AI and Static Analysis Translate Legacy Bioinformatics Code to Rust
A novel method that integrates agentic AI with static analysis facilitates the conversion of outdated bioinformatics code into the contemporary Rust language, tackling challenges related to technical debt, security, and performance. This technique was tested on widely used software for next-generation sequencing (NGS) and imaging, particularly demonstrated with the software Bascet, which saw its size shrink by nearly 80 times, its build time cut by around 10 times, and key steps’ performance enhanced by over 3 times. Additionally, Unix dependencies were eliminated, making Bascet the sole single-cell pipeline capable of operating without such dependencies. Published on arXiv (ID: 2608.13029), the research offers prompts and tools for systematic code translation, aiming to upgrade legacy code in languages like Perl and Fortran, often lacking maintainers and posing safety concerns in clinical environments. This work underscores the promise of AI-driven code translation to boost efficiency, lessen environmental impact, and utilize modern hardware effectively.
Key facts
- Agentic AI combined with static analysis translates legacy bioinformatics code to Rust.
- The method was evaluated on common software for NGS and imaging.
- Bascet's size was reduced by ~80x.
- Build time for Bascet decreased by ~10x.
- Performance of key steps in Bascet improved >3x.
- Unix dependencies were removed from Bascet.
- Bascet is now the only single-cell pipeline without Unix dependencies.
- The research is published on arXiv with ID 2608.13029.
Entities
Institutions
- arXiv