CMMI-DEV Organizational Process Performance

Overview

Draft CMMI-DEV Organizational Process Performance (OPP) deliverables: process performance baselines and process performance models for ML4.

Getting Started

Add your source materials to the project workspace, then activate the CMMI-DEV Organizational Process Performance skill. The agent will guide you through the process step by step.

Example Conversation

You: We’re building process performance baselines for CMMI OPP. We have historical data on effort, schedule variance, and defect density from the last 5 projects. Can you draft the baseline document and a simple process performance model for estimation?

Agent: I’ll draft an OPP document: (1) process performance baselines — which processes and measures, data period, statistical summary (e.g. mean, std dev, percentiles); (2) process performance model(s) — e.g. effort or defect prediction from size/type. I’ll use your historical data structure; you can paste summary stats or I’ll propose a template for capturing them.

You: Our measures are: effort (hours), schedule variance (days), defect density (defects per KLOC). We use story points for size. Can we have a model for effort from story points?

Agent: I’ve created process-performance-baselines.md with baseline definitions for effort, schedule variance, and defect density (with placeholders for mean, std dev, N). I’ve added a simple process performance model: effort = f(story points) with a linear form and notes on calibrating from historical data. The model is in process-performance-models.md. You can plug in your numbers.

Sample Output Excerpt

Excerpt from Process Performance Baselines produced with this skill.

Process performance baseline — Effort

Process: Development (design through code complete). Measure: Actual effort (hours). Data: Last 5 projects; size range 200–800 story points.

Use: Setting project effort expectations; comparing project performance to baseline; input to process performance models.

Process performance model — Effort estimation

Purpose: Predict project effort from size. Form: Effort (hours) = a × Story points + b. Calibration: Fit a, b from historical (project, story points, actual effort). Usage: Estimate new project effort; what-if for scope changes.