pmat 3.11.0

PMAT - Zero-config AI context generation and code quality toolkit (CLI, MCP, HTTP)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

// Factor calculation: complexity gradient and heuristic complexity
// Churn-related code moved to tdg_calculator_factors_churn.rs

fn is_function_start(trimmed: &str) -> bool {
    trimmed.starts_with("fn ")
        || trimmed.starts_with("def ")
        || trimmed.starts_with("function ")
        || trimmed.starts_with("func ")
}

fn is_control_flow(trimmed: &str) -> bool {
    trimmed.starts_with("if ")
        || trimmed.starts_with("elif ")
        || trimmed.starts_with("while ")
        || trimmed.starts_with("for ")
        || trimmed.starts_with("match ")
        || trimmed.starts_with("case ")
}

fn score_from_complexities(function_complexities: &[usize], line_count: usize) -> f64 {
    let mean = function_complexities.iter().sum::<usize>() as f64
        / function_complexities.len() as f64;
    let variance = function_complexities
        .iter()
        .map(|&c| (c as f64 - mean).powi(2))
        .sum::<f64>()
        / function_complexities.len() as f64;

    let base_complexity = mean / 5.0;
    let variance_factor = variance.sqrt() / 3.0;
    let max_complexity = function_complexities.iter().max().copied().unwrap_or(0) as f64;
    let hotspot_factor = (max_complexity / 10.0).min(1.0);
    let loc_factor = (line_count as f64 / 100.0).min(1.0);

    let score =
        base_complexity * 0.4 + variance_factor * 0.2 + hotspot_factor * 0.2 + loc_factor * 0.2;
    score.min(5.0)
}

impl TDGCalculator {
    /// Compute complexity gradient with variance analysis
    #[allow(dead_code)]
    fn compute_complexity_gradient(&self, ast: &UnifiedAstNode) -> ComplexityVariance {
        let mut analyzer = VerifiedComplexityAnalyzer::new();
        let complexities: Vec<u32> = if matches!(ast.kind, AstKind::Function(_)) {
            vec![analyzer.analyze_function(ast).cyclomatic]
        } else {
            vec![]
        };

        if complexities.is_empty() {
            return ComplexityVariance {
                mean: 0.0,
                variance: 0.0,
                gini: 0.0,
                percentile_90: 0.0,
            };
        }

        let sum: u32 = complexities.iter().sum();
        let mean = f64::from(sum) / complexities.len() as f64;

        let squared_diff_sum: f64 = complexities
            .iter()
            .map(|&c| (f64::from(c) - mean).powi(2))
            .sum();
        let variance = squared_diff_sum / complexities.len() as f64;

        let mut sorted = complexities;
        sorted.sort_unstable();

        let mut gini_sum = 0.0;
        for (i, &value) in sorted.iter().enumerate() {
            gini_sum += (2.0 * (i + 1) as f64 - sorted.len() as f64 - 1.0) * f64::from(value);
        }
        let gini = if sum == 0 {
            0.0
        } else {
            gini_sum / (sorted.len() as f64 * f64::from(sum))
        };

        let percentile_idx = ((sorted.len() as f64 * 0.9) as usize).min(sorted.len() - 1);
        let percentile_90 = f64::from(sorted[percentile_idx]);

        ComplexityVariance {
            mean,
            variance,
            gini,
            percentile_90,
        }
    }

    /// Calculate complexity factor (normalized 0-5)
    async fn calculate_complexity_factor(&self, path: &Path) -> Result<f64> {
        let content = tokio::fs::read_to_string(path).await?;
        let lines: Vec<&str> = content.lines().collect();

        let mut complexity = 0usize;
        let mut nesting_level = 0usize;
        let mut function_complexities = Vec::<usize>::new();

        for line in &lines {
            let trimmed = line.trim();

            if is_function_start(trimmed) {
                if complexity > 0 {
                    function_complexities.push(complexity);
                }
                complexity = 1;
                nesting_level = 0;
            }

            if is_control_flow(trimmed) {
                complexity += 1 + nesting_level;
            }

            nesting_level += trimmed.matches('{').count();
            nesting_level = nesting_level.saturating_sub(trimmed.matches('}').count());
        }

        if complexity > 0 {
            function_complexities.push(complexity);
        }

        if function_complexities.is_empty() {
            return Ok(0.5);
        }

        Ok(score_from_complexities(&function_complexities, lines.len()))
    }
}