pmat 3.16.0

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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154

impl CudaSimdAnalyzer {

    fn score_falsifiability(
        defects: &[DetectedDefect],
        barrier_safety: &BarrierSafetyResult,
        patterns: &RustProjectPatterns,
    ) -> FalsifiabilityScore {
        let p0_defects = defects
            .iter()
            .filter(|d| d.defect_class.severity == DefectSeverity::P0Critical)
            .count();

        FalsifiabilityScore {
            barrier_safety: if barrier_safety.unsafe_barriers.is_empty() {
                5.0
            } else {
                5.0 * barrier_safety.safety_score
            },
            bounds_verification: if p0_defects == 0 {
                if patterns.has_safety_comments { 5.0 } else { 4.0 }
            } else {
                2.5
            },
            divergence_testing: if patterns.has_proptest_regressions { 5.0 } else { 2.5 },
            memory_race_detection: if patterns.has_miri_config { 5.0 } else { 2.5 },
            occupancy_bounds: 5.0,
        }
    }

    fn score_reproducibility(patterns: &RustProjectPatterns) -> ReproducibilityScore {
        ReproducibilityScore {
            deterministic_output: if patterns.has_golden_traces { 8.0 } else { 4.0 },
            version_pinning: if patterns.has_cargo_lock && patterns.has_rust_toolchain {
                5.0
            } else if patterns.has_cargo_lock {
                3.5
            } else {
                2.5
            },
            hardware_specification: 2.5,
            benchmark_harness: if patterns.has_criterion_benches { 4.0 } else { 2.0 },
            ci_cd_integration: if patterns.has_github_ci { 3.0 } else { 1.5 },
        }
    }

    fn score_statistical_rigor(patterns: &RustProjectPatterns) -> StatisticalRigorScore {
        if patterns.has_criterion_benches {
            StatisticalRigorScore {
                warmup_iterations: 4.0,
                sample_count: 4.0,
                outlier_analysis: 4.0,
                confidence_intervals: 3.0,
            }
        } else {
            StatisticalRigorScore {
                warmup_iterations: 2.0,
                sample_count: 2.0,
                outlier_analysis: 2.0,
                confidence_intervals: 1.5,
            }
        }
    }

    fn score_historical_integrity(
        defects: &[DetectedDefect],
        patterns: &RustProjectPatterns,
    ) -> HistoricalIntegrityScore {
        HistoricalIntegrityScore {
            fault_lineage: if patterns.has_changelog {
                4.0
            } else if !defects.is_empty() {
                3.0
            } else {
                2.0
            },
            regression_tests: if patterns.has_proptest_regressions { 3.0 } else { 1.5 },
            root_cause_documentation: if patterns.has_changelog { 3.0 } else { 1.5 },
        }
    }

    fn calculate_score(
        &self,
        defects: &[DetectedDefect],
        barrier_safety: &BarrierSafetyResult,
        coalescing: &CoalescingResult,
        path: &Path,
    ) -> PopperScore {
        let patterns = self.detect_rust_patterns(path);

        let falsifiability = Self::score_falsifiability(defects, barrier_safety, &patterns);
        let reproducibility = Self::score_reproducibility(&patterns);
        let transparency = TransparencyScore {
            ptx_inspection: 3.0,
            register_allocation: 2.5,
            occupancy_calculation: 2.5,
            memory_layout: 2.0,
        };
        let statistical_rigor = Self::score_statistical_rigor(&patterns);
        let historical_integrity = Self::score_historical_integrity(defects, &patterns);
        let gpu_simd_specific = GpuSimdSpecificScore {
            warp_efficiency: 1.0,
            memory_throughput: coalescing.efficiency * 2.0,
            instruction_mix: 0.5,
        };

        PopperScore::calculate(
            falsifiability,
            reproducibility,
            transparency,
            statistical_rigor,
            historical_integrity,
            gpu_simd_specific,
        )
    }

    fn build_kaizen_metrics(&self, defects: &[DetectedDefect]) -> KaizenMetrics {
        let ticket_references: Vec<String> = defects
            .iter()
            .map(|d| d.defect_class.ticket_id.clone())
            .collect();

        let resolved_count = defects.iter().filter(|d| d.defect_class.resolved).count() as u32;

        KaizenMetrics {
            tickets_resolved: resolved_count,
            mttd: 24.0,            // Default estimate
            mttf: 48.0,            // Default estimate
            escape_rate: 0.05,     // 5% default
            regression_rate: 0.02, // 2% default
            ticket_references,
        }
    }

    /// Check if quality gate passes
    #[must_use]
    #[provable_contracts_macros::contract("pmat-core.yaml", equation = "check_compliance")]
    pub fn passes_quality_gate(&self, result: &CudaSimdTdgResult) -> bool {
        if !result.score.gateway_passed {
            return false;
        }

        if self.config.fail_on_p0 {
            let has_p0 = result
                .defects
                .iter()
                .any(|d| d.defect_class.severity == DefectSeverity::P0Critical);
            if has_p0 {
                return false;
            }
        }

        result.score.total >= self.config.min_score
    }
}