Enum Commands 

pub enum Commands {
    Analyze {
        org: String,
        output: PathBuf,
        max_concurrent: usize,
        model: Option<PathBuf>,
        ml_confidence: f32,
    },
    Summarize {
        input: PathBuf,
        output: PathBuf,
        strip_pii: bool,
        top_n: usize,
        min_frequency: usize,
        include_examples: bool,
    },
    ReviewPr {
        baseline: PathBuf,
        files: String,
        format: String,
        output: Option<PathBuf>,
    },
    ExtractTrainingData {
        repo: PathBuf,
        output: PathBuf,
        min_confidence: f32,
        max_commits: usize,
        create_splits: bool,
        viz: bool,
    },
    TrainClassifier {
        input: PathBuf,
        output: Option<PathBuf>,
        n_estimators: usize,
        max_depth: usize,
        max_features: usize,
    },
    Export {
        repo: PathBuf,
        output: PathBuf,
        format: String,
        max_commits: usize,
        min_confidence: f32,
    },
    ImportDepyler {
        input: PathBuf,
        output: PathBuf,
        min_confidence: f32,
        merge: Option<PathBuf>,
        create_splits: bool,
    },
    Localize {

        passed_coverage: PathBuf,
        failed_coverage: PathBuf,
        passed_count: usize,
        failed_count: usize,
        formula: String,
        top_n: usize,
        output: PathBuf,
        format: String,
        enrich_tdg: bool,
        repo: Option<PathBuf>,
        rag: bool,
        knowledge_base: Option<PathBuf>,
        fusion: String,
        similar_bugs: usize,
        ensemble: bool,
        ensemble_model: Option<PathBuf>,
        include_churn: bool,
        calibrated: bool,
        calibration_model: Option<PathBuf>,
        confidence_threshold: f32,
    
},
}

Variants

Analyze

Analyze GitHub organization for defect patterns

Fields

org: String

Organization name

output: PathBuf

Output file path

max_concurrent: usize

Maximum concurrent repository analysis

model: Option<PathBuf>

Path to trained ML model (optional, uses rule-based if not provided)

ml_confidence: f32

Confidence threshold for ML predictions (0.0-1.0)

Summarize

Summarize analysis report for AI consumption (Phase 2)

Fields

input: PathBuf

Input YAML report from ‘analyze’ command

output: PathBuf

Output summary file

strip_pii: bool

Strip PII (author names, commit hashes, email addresses)

top_n: usize

Top N defect categories to include

min_frequency: usize

Minimum frequency to include

include_examples: bool

Include anonymized examples (with PII redacted if strip-pii is true)

ReviewPr

Review PR with organizational context (Phase 3)

Fields

baseline: PathBuf

Baseline summary from weekly analysis

files: String

Files changed in PR (comma-separated)

format: String

Output format: markdown, json

output: Option<PathBuf>

Output file (stdout if not specified)

ExtractTrainingData

Extract training data from Git repository (Phase 2 ML)

Fields

repo: PathBuf

Path to Git repository

output: PathBuf

Output JSON file

min_confidence: f32

Minimum confidence threshold (0.0-1.0)

max_commits: usize

Maximum commits to analyze

create_splits: bool

Create train/validation/test splits

viz: bool

Show visualization (requires –features viz)

TrainClassifier

Train ML classifier on extracted training data (Phase 2 ML)

Fields

input: PathBuf

Input training data JSON file

output: Option<PathBuf>

Output model file (optional)

n_estimators: usize

Number of trees in Random Forest

max_depth: usize

Maximum tree depth

max_features: usize

Maximum TF-IDF features

Export

Export CommitFeatures to aprender-compatible format (Issue #2)

Fields

repo: PathBuf

Path to Git repository to analyze

output: PathBuf

Output file path

format: String

Export format: json, binary, parquet

max_commits: usize

Maximum commits to analyze

min_confidence: f32

Minimum confidence threshold for classification (0.0-1.0)

ImportDepyler

Import Depyler CITL corpus for ground-truth training labels (NLP-014)

Fields

input: PathBuf

Path to Depyler JSONL export file

output: PathBuf

Output training data JSON file

min_confidence: f32

Minimum confidence threshold (0.0-1.0)

merge: Option<PathBuf>

Merge with existing training data file (optional)

create_splits: bool

Create train/validation/test splits

Localize

Localize faults using Tarantula SBFL (Spectrum-Based Fault Localization)

Fields

passed_coverage: PathBuf

Path to LCOV coverage file from passing tests

failed_coverage: PathBuf

Path to LCOV coverage file from failing tests

passed_count: usize

Number of passing tests

failed_count: usize

Number of failing tests

formula: String

SBFL formula: tarantula, ochiai, dstar2, dstar3

top_n: usize

Top N suspicious statements to report

output: PathBuf

Output file path

format: String

Output format: yaml, json, terminal

enrich_tdg: bool

Include TDG scores from pmat (requires pmat)

repo: Option<PathBuf>

Repository path for TDG enrichment

rag: bool

Enable RAG-enhanced localization with trueno-rag

knowledge_base: Option<PathBuf>

Path to bug knowledge base YAML file (for RAG)

fusion: String

Fusion strategy for RAG: rrf, linear, dbsf, sbfl-only

similar_bugs: usize

Number of similar bugs to retrieve (for RAG)

ensemble: bool

Enable weighted ensemble model (Phase 6)

ensemble_model: Option<PathBuf>

Path to trained ensemble model file

include_churn: bool

Include churn metrics from git history (for ensemble)

calibrated: bool

Enable calibrated probability output (Phase 7)

calibration_model: Option<PathBuf>

Path to trained calibration model file

confidence_threshold: f32

Confidence threshold for calibrated predictions (0.0-1.0)

Trait Implementations

impl Debug for Commands

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl FromArgMatches for Commands

fn from_arg_matches(__clap_arg_matches: &ArgMatches) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn from_arg_matches_mut( __clap_arg_matches: &mut ArgMatches, ) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn update_from_arg_matches( &mut self, __clap_arg_matches: &ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

fn update_from_arg_matches_mut<'b>( &mut self, __clap_arg_matches: &mut ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

impl Subcommand for Commands

fn augment_subcommands<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate Self via FromArgMatches::from_arg_matches_mut

fn augment_subcommands_for_update<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate self via FromArgMatches::update_from_arg_matches_mut

fn has_subcommand(__clap_name: &str) -> bool

Test whether Self can parse a specific subcommand