Ragex.Analysis.Suggestions.Ranker (Ragex v0.3.1)

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Priority ranking system for refactoring suggestions.

Scores suggestions based on multiple factors:

  • Benefit (40%): Expected improvement from refactoring
  • Impact (20%): Scope of change (number of affected files/modules)
  • Risk (20%): Likelihood of introducing bugs (subtracted)
  • Effort (10%): Time/complexity to implement (subtracted)
  • Confidence (10%): Confidence in the detection

Priority Levels

  • critical (score > 0.8): Must address soon, high impact issues
  • high (score > 0.6): Important improvements with good ROI
  • medium (score > 0.4): Beneficial but not urgent
  • low (score > 0.2): Optional improvements
  • info (score <= 0.2): For awareness only

Examples

alias Ragex.Analysis.Suggestions.Ranker

suggestion = %{
  pattern: :extract_function,
  confidence: 0.85,
  benefit_score: 0.8,
  effort_score: 0.5,
  impact: %{affected_files: 3, risk: :medium}
}

scored = Ranker.score_suggestion(suggestion)
# => %{...suggestion, priority: :high, priority_score: 0.72}

Summary

Functions

adjust_for_pattern(suggestion)

Adjusts priority score based on pattern-specific factors.

calculate_roi(suggestion)

Calculates ROI (Return on Investment) for a suggestion.

calculate_statistics(suggestions)

Calculates statistics for a list of suggestions.

classify_priority(score)

Classifies a numeric priority score into a priority level.

compare_priority(sugg1, sugg2)

Compares two suggestions for sorting.

explain_score(suggestion)

Generates a human-readable explanation of the scoring.

filter_by_priority(suggestions, min_priority)

Filters suggestions by minimum priority level.

group_by_priority(suggestions)

Groups suggestions by priority level.

score_suggestion(suggestion)

Scores a suggestion and assigns a priority level.

Functions

adjust_for_pattern(suggestion)

Adjusts priority score based on pattern-specific factors.

Some patterns are inherently more important:

  • Dead code removal: Low risk, easy win
  • Complexity reduction: High benefit
  • Coupling reduction: Medium-high effort, high benefit

calculate_roi(suggestion)

Calculates ROI (Return on Investment) for a suggestion.

ROI = Benefit / Effort

Higher ROI means better return for the effort invested.

calculate_statistics(suggestions)

Calculates statistics for a list of suggestions.

Returns map with:

  • :total - Total number of suggestions
  • :by_priority - Count by priority level
  • :average_score - Average priority score
  • :average_roi - Average ROI
  • :high_priority_count - Count of high + critical

classify_priority(score)

Classifies a numeric priority score into a priority level.

Examples

iex> Ranker.classify_priority(0.85)
:critical

iex> Ranker.classify_priority(0.65)
:high

iex> Ranker.classify_priority(0.15)
:info

compare_priority(sugg1, sugg2)

Compares two suggestions for sorting.

Returns:

  • :gt if first has higher priority
  • :lt if second has higher priority
  • :eq if equal priority

explain_score(suggestion)

Generates a human-readable explanation of the scoring.

Examples

explanation = Ranker.explain_score(suggestion)
IO.puts(explanation)

filter_by_priority(suggestions, min_priority)

Filters suggestions by minimum priority level.

Examples

suggestions
|> Ranker.filter_by_priority(:high)
# Returns only :critical and :high priority suggestions

group_by_priority(suggestions)

Groups suggestions by priority level.

Returns

Map with priority levels as keys and lists of suggestions as values.

score_suggestion(suggestion)

Scores a suggestion and assigns a priority level.

Parameters

  • suggestion - Raw suggestion map from pattern detector

Returns

  • Suggestion with added :priority and :priority_score fields