A multi-agent platform for assessment and improvement of bioinformatics software documentation

Area of Science:

• Bioinformatics and computational biology
• Software engineering in life sciences

Background:

• Bioinformatics tools are advancing rapidly, but documentation quality lags, hindering usability and reproducibility.
• Poor documentation limits tool adoption, impedes cross-laboratory reproducibility, and reduces the impact of computational methods.

Purpose of the Study:

• Introduce BioGuider, a multi-agent platform to systematically evaluate and enhance bioinformatics software documentation.
• Treat documentation as a first-class, testable object within the software development lifecycle.

Main Methods:

• BioGuider employs a modular pipeline for documentation collection, assessment, reporting, and correction.
• Specialized agents emulate user interactions to evaluate documentation against task-oriented criteria (installation, configuration, usage, tutorials).
• A controlled error-injection framework and multiple large language models were used for benchmarking.

Main Results:

• BioGuider demonstrates robust error detection and correction capabilities, even with significant documentation degradation.
• The platform supports iterative refinement while maintaining code integrity and biological context.
• A positive association was observed between documentation quality and citation frequency across 47 tools.

Conclusions:

• High-quality documentation is a critical, yet under-quantified, driver of bioinformatics software adoption and scientific impact.
• BioGuider offers a systematic approach to improving documentation, thereby enhancing the reliability and reach of biomedical research tools.