One of the most valuable benefits of AI UX testing is the ability to discover where users get stuck. These pain points are often invisible in manual testing. With simulated agents, you can track which paths are confusing, which steps fail, and where users drop off.
Types of Pain Points to Track
- Dwell time: How long users stay on a cell (or screen)
- Looping paths: Users repeat the same actions
- Abandonment: Users fail to reach the goal
Step 1: Track Cell Visits
In model.py, initialize a grid visit counter:
self.cell_visits = {}
for x in range(width):
for y in range(height):
self.cell_visits[(x, y)] = 0
Step 2: Update Visit Count in Agent Movement
In agent.py, increment the visit count every step:
self.model.cell_visits[self.pos] += 1
Step 3: Output the Heatmap Data
At the end of each run, you can export or log the most visited cells:
top_cells = sorted(self.cell_visits.items(), key=lambda x: x[1], reverse=True)
for pos, count in top_cells[:10]:
print(f"Cell {pos} visited {count} times")
Optional: Visualize a Heatmap
If you’re comfortable with Python plotting, add:
import matplotlib.pyplot as plt
import numpy as np
heat = np.zeros((width, height))
for (x, y), count in self.cell_visits.items():
heat[y][x] = count
plt.imshow(heat, cmap="hot", interpolation="nearest")
plt.title("AI UX Testing Heatmap")
plt.colorbar()
plt.show()
Why This Matters
With these metrics, you can detect:
- Dead zones in your layout
- Steps that frustrate users
- Areas where improvements are most urgent
Up Next: Visual Feedback with Charts
In Day 7, we’ll enhance our AI UX testing toolkit by visualizing user behavior paths with charts, bar graphs, and interactive dashboards.
Tag: #AIUXTesting #UXPainPoints #UserAnalytics
