The Engineering Quality Risk Indicators (EQRI) initiative aims to measure risk to engineering quality as a counterpoint to existing cost and schedule metrics and to give PDTs and Technical Leads a tool to help them leverage their voice for quality.
The purpose of this website is to provide a detailed Exploratory Data Analysis (EDA) summary of the FY25 version of the EQRI questionnaire. To explore these results, use the items on the top menu:
- Questions - Results for each question are presented and summarized by key USACE organizational units.
- Indicators - Results for each indicator (a metric derived from a topical grouping of questions) summarized by key organizational units.
This project uses GitHub Copilot to assist with:
- R function development and refactoring
- Test case generation
- Documentation writing
- Package structure and best practices
Transparency: Unfiltered AI session logs are maintained in dev/sessions/ for colleague review and audit purposes. All AI suggestions undergo human review, testing, and validation before integration.
Accountability: The project maintainer (@mpdougherty) is responsible for all code quality, correctness, and scientific validity regardless of AI assistance.
Human Oversight: AI-generated code is:
- Validated against official package documentation (see
DESCRIPTIONfor dependencies) - Tested using
{testthat}framework - Reviewed for alignment with project goals and data requirements
- Modified as needed to meet USACE data quality standards