complexity-scorer-5-confidence-scoring
Über
Diese Teilfähigkeit fügt Komplexitätsschätzungen einen Konfidenzwert hinzu, der die Zuverlässigkeit der Bewertung anzeigt. Sie berechnet die Konfidenz basierend auf Textlänge und Keyword-Übereinstimmungen und bietet Entwicklern ein Maß für die Gewissheit automatisierter Bewertungen. Nutzen Sie sie, wenn Sie einschätzen müssen, wie vertrauenswürdig eine Komplexitätsklassifizierung ist.
Schnellinstallation
Claude Code
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Dokumentation
5. Confidence Scoring (+1)
5. Confidence Scoring
Add confidence to scoring:
#!/bin/bash
# ABOUTME: Confidence scoring for complexity estimates
# ABOUTME: Indicates reliability of the score
# Calculate confidence
calculate_confidence() {
local text="$1"
local confidence=50 # Base confidence
local word_count=$(echo "$text" | wc -w)
local keyword_matches=0
local text_lower=$(echo "$text" | tr '[:upper:]' '[:lower:]')
# More words = higher confidence (more context)
if [[ $word_count -gt 15 ]]; then
((confidence+=20))
elif [[ $word_count -gt 8 ]]; then
((confidence+=10))
elif [[ $word_count -lt 3 ]]; then
((confidence-=20))
fi
# Keyword matches boost confidence
for pattern in "$HIGH_COMPLEXITY" "$MEDIUM_COMPLEXITY" "$LOW_COMPLEXITY"; do
if echo "$text_lower" | grep -qE "$pattern"; then
((keyword_matches++))
fi
done
((confidence += keyword_matches * 10))
# Cap confidence
[[ $confidence -gt 100 ]] && confidence=100
[[ $confidence -lt 10 ]] && confidence=10
echo $confidence
}
# Get confidence label
confidence_label() {
local confidence="$1"
if [[ $confidence -ge 80 ]]; then
echo "High"
elif [[ $confidence -ge 60 ]]; then
echo "Medium"
elif [[ $confidence -ge 40 ]]; then
echo "Low"
else
echo "Very Low"
fi
}
6. Historical Learning
Adjust based on past accuracy:
#!/bin/bash
# ABOUTME: Historical learning for complexity scoring
# ABOUTME: Track and adjust based on actual outcomes
HISTORY_FILE="${HOME}/.complexity-scorer/history.log"
# Log prediction vs actual
log_outcome() {
local task="$1"
local predicted_score="$2"
local actual_complexity="$3" # user-provided feedback
local timestamp=$(date '+%Y-%m-%d_%H:%M:%S')
mkdir -p "$(dirname "$HISTORY_FILE")"
echo "${timestamp}|${predicted_score}|${actual_complexity}|${task}" >> "$HISTORY_FILE"
}
# Calculate prediction accuracy
calculate_accuracy() {
local correct=0
local total=0
while IFS='|' read -r ts predicted actual task; do
[[ "$ts" =~ ^#.*$ ]] && continue
[[ -z "$ts" ]] && continue
((total++))
local predicted_class=$(classify_complexity "$predicted")
if [[ "$predicted_class" == "$actual" ]]; then
((correct++))
fi
done < "$HISTORY_FILE"
if [[ $total -gt 0 ]]; then
echo $((correct * 100 / total))
else
echo 0
fi
}
# Find common misclassifications
find_patterns() {
echo "Analyzing misclassification patterns..."
while IFS='|' read -r ts predicted actual task; do
local predicted_class=$(classify_complexity "$predicted")
if [[ "$predicted_class" != "$actual" ]]; then
echo "Predicted: $predicted_class, Actual: $actual"
echo "Task: $task"
echo "---"
fi
done < "$HISTORY_FILE"
}
GitHub Repository
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