Why has the average time to resolve critical issues reported through Chainguard's policy-as-code feature doubled in the last month?

Introduction

The doubling of average resolution time for critical issues in Chainguard's policy-as-code feature is a concerning trend that requires immediate attention. This analysis will systematically investigate potential root causes, validate hypotheses, and propose targeted solutions to address this performance decline.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent product update. Has there been any significant change to the policy-as-code feature in the last 1-2 months?

Why it matters: Recent changes could directly impact issue resolution time. Expected answer: Yes, there was a major update. Impact on approach: If yes, we'd focus on the update's impact; if no, we'd look at other factors.

• Considering user segments, I'm curious about the distribution of these critical issues. Are we seeing this increase across all customer types, or is it concentrated in a specific segment?

Why it matters: Helps identify if the problem is universal or segment-specific. Expected answer: The increase is seen across all segments. Impact on approach: If segment-specific, we'd tailor our solution; if universal, we'd look at system-wide factors.

• Thinking about the metric itself, has there been any change in how we define or measure "critical issues" or "resolution time" in the last month?

Why it matters: Ensures we're comparing apples to apples in our analysis. Expected answer: No changes in metric definition or measurement. Impact on approach: If changed, we'd need to account for the new methodology in our analysis.

• Considering external factors, have we seen any significant increase in the volume of critical issues reported during this period?

Why it matters: A surge in issues could explain longer resolution times. Expected answer: Issue volume has remained relatively stable. Impact on approach: If volume increased, we'd focus on scaling our response; if not, we'd look at internal processes.