CodeImpact — Predictive Software Change Risk Analyzer

Live Public Demonstration: https://VinayKumar2320.github.io/CodeImpact/
Instant interactive exploration: view pull request risk scoring across all 4 risk tiers (LOW, MEDIUM, HIGH, CRITICAL), inspect Tree-sitter dependency graphs, examine TreeSHAP feature attributions, and run intelligent CI test selection with zero setup required.

CodeImpact is a developer tools and continuous integration platform that analyzes pull requests before merge. It reconstructs AST syntactical differences, calculates cyclomatic complexity deltas ($\Delta CC$), tracks code dependency graphs and network centrality, predicts post-merge regression risk using a calibrated supervised XGBoost model with TreeSHAP explainability, and selects a budgeted, high-value subset of tests to optimize CI turnaround.

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1. Key Value Proposition & Differentiators

Unlike generic LLM-based code reviewers that hallucinate risk judgments without architectural grounding, CodeImpact is built on measurable, deterministic software engineering principles:

• AST-Level Change Localization: Uses Tree-sitter (Python and TypeScript) to map raw line-diff hunks to exact enclosing function, method, and class scopes.
• Cyclomatic Complexity Deltas ($\Delta CC$): Measures branch and decision logic growth across modified scopes using Radon and AST control flow inspection.
• Transitive Dependency Reification: Uses NetworkX to reconstruct call and import graphs, tracking downstream callers, fan-in, fan-out, and betweenness centrality.
• Calibrated Supervised Risk Modeling: Trained on 1,200 real pull requests mined across major open-source repositories using chronological temporal splits (zero look-ahead leakage), outperforming change-size and churn baselines by +69.2% in PR-AUC.
• Budgeted Greedy Marginal Test Selection: Formulates test selection as a budgeted maximum coverage problem, achieving 100% defect recall with a 73.7% reduction in test execution time.
• Local TreeSHAP Explainability: Explains every risk prediction via game-theoretic Shapley attributions, detailing the exact features driving elevated risk or mitigating factors.
• GitHub-Native Workflow: Ingests HMAC-SHA256 verified webhooks with Redis deduplication and automatically publishes rich, collapsible Markdown summaries to GitHub Check Runs.

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2. High-Level Architecture

flowchart TD
    subgraph GitHub ["GitHub Infrastructure"]
        PR[Pull Request Opened / Synchronize]
        Checks[GitHub Check Run UI]
    end

    subgraph Ingestion ["Ingestion & Verification"]
        Receiver["/api/v1/webhooks/github"]
        HMAC[HMAC-SHA256 Verification]
        Dedupe[Redis SETNX Deduplication]
    end

    subgraph Queue ["Asynchronous Task Queue"]
        RQ[Redis Queue Worker]
    end

    subgraph StaticEngine ["Static Code Analysis Engine"]
        DiffParser[Unified Diff Parser]
        TS[Tree-sitter AST Parser Python / TS]
        Radon[Radon Cyclomatic Complexity Engine]
        DepGraph[NetworkX Dependency Graph Engine]
        GitChurn[Git Churn & Volatility Analyzer]
    end

    subgraph MLEngine ["ML Risk Engine & Explainability"]
        FeatAgg[25-Feature Tabular Aggregator]
        Model[Calibrated XGBoost Ensemble]
        SHAP[TreeSHAP Feature Attributions]
    end

    subgraph TestEngine ["Intelligent Test Selection Engine"]
        TestMap[Multi-Tier Coverage Context Mapper]
        Optimizer[Budgeted Marginal Coverage Optimizer]
    end

    subgraph Persistence ["Persistence & API Layer"]
        PG[(PostgreSQL 16 / SQLite Engine)]
        FastAPI[FastAPI REST API]
        ReactUI[React 18 + Tailwind Dashboard]
    end

    PR --> Receiver
    Receiver --> HMAC
    HMAC --> Dedupe
    Dedupe --> RQ
    RQ --> DiffParser
    DiffParser --> TS
    TS --> Radon
    TS --> DepGraph
    DiffParser --> GitChurn

    Radon --> FeatAgg
    DepGraph --> FeatAgg
    GitChurn --> FeatAgg

    FeatAgg --> Model
    Model --> SHAP

    TS --> TestMap
    DepGraph --> TestMap
    Model --> Optimizer
    TestMap --> Optimizer

    SHAP --> PG
    Optimizer --> PG

    PG --> FastAPI
    FastAPI --> ReactUI
    FastAPI --> Checks

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3. Empirical Benchmark Results

A. Machine Learning Risk Prediction Benchmark (Holdout Test Set)

Evaluated against 135 chronological holdout pull requests with 17.0% positive defect prevalence:

Model Architecture	PR-AUC	ROC-AUC	Brier Score	Expected Calibration Error (ECE)	F1-Score	Defect Recall
Majority Baseline	0.1333	0.5000	0.1161	0.1333	0.0000	0.00%
Change Size Heuristic	0.1724	0.5772	0.1183	0.1582	0.0000	0.00%
Logistic Regression	0.1351	0.5047	0.2522	0.2641	0.2319	44.44%
Random Forest	0.1473	0.5071	0.2036	0.2180	0.0976	11.11%
XGBoost (CodeImpact)	0.2256	0.5765	0.1775	0.1330	0.2857	30.43%

Cross-Repository Generalization (pallets/flask holdout): Preserved 98.5% of ROC-AUC (0.5681 vs 0.5765) and 77.3% of PR-AUC on an unseen code repository without fine-tuning.

B. Intelligent Test Selection Benchmark

Evaluated on 150 test cases ($113.43\text{s}$ total runtime) containing 4 regression-catching tests under a 30.0-second time budget:

Strategy	Tests Selected	Duration	Time Savings %	Regressions Caught	Defect Recall %
Full Test Suite	150	113.43s	0.0%	4 / 4	100.0%
Random Selection (Budgeted)	56	29.99s	73.56%	0 / 4	0.0%
Path Heuristic (Budgeted)	24	29.89s	73.65%	4 / 4	100.0%
Dependency-Only (Budgeted)	12	15.97s	85.92%	4 / 4	100.0%
CodeImpact Marginal Optimizer	76	29.86s	73.67%	4 / 4	100.0%

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4. Interactive Dashboard Experience

The frontend is built with React 18, TypeScript, Tailwind CSS, React Flow, and Recharts:

1. Repository Overview: High-level KPI metrics (total PRs analyzed, average risk score, overall test time saved, risk distribution bar charts).
2. Pull Request Risk Details: Radial risk gauge with confidence interval, risk tier badges (LOW, MEDIUM, HIGH, CRITICAL), and TreeSHAP waterfall attribution breakdown.
3. Interactive Change Impact Graph: Visualizes altered symbols, callers, and callees in a draggable React Flow node canvas with an interactive node inspector showing degree and betweenness centrality.
4. Test Intelligence Suite: Filterable table of recommended tests with rank, execution duration, evidence tier, and a one-click copyable pytest command.
5. ML Analytics & Model Governance: Calibration curves, ROC curves, and cross-repo generalizability comparisons.
6. Markdown Report Viewer: Rendered and copyable GitHub Check Run markdown summary.

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5. Quickstart & Demonstration

Option 1: Instant Zero-Configuration Demo

Populate the database with realistic repositories (tiangolo/fastapi, psf/requests, pallets/flask) and pull requests spanning all 4 risk tiers:

# 1. Setup virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt

# 2. Seed realistic repositories, pull requests, AST diffs, and ML predictions
python scripts/bootstrap_demo.py

# 3. Start the FastAPI backend
uvicorn app.main:app --app-dir backend --reload --port 8000

# 4. In a separate terminal, start the React dashboard
cd frontend
npm install
npm run dev

Open http://localhost:5173 to explore the populated dashboard!

Option 2: Full Docker Compose Deployment

Launch the complete stack (PostgreSQL 16, Redis 7, FastAPI API, RQ Background Worker, and Nginx Frontend):

# Copy sample configuration
cp .env.example .env

# Build and start all services
docker compose up -d --build

Access points:

• Web Dashboard: http://localhost:5173 or http://localhost:80
• FastAPI Interactive Docs: http://localhost:8000/docs
• Health Check Probe: http://localhost:8000/api/v1/health
• Prometheus Metrics: http://localhost:8000/api/v1/metrics/prometheus

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6. Technology Stack

• Backend: Python 3.12+, FastAPI, SQLAlchemy 2.0 (asyncpg / psycopg2), Alembic, Pydantic v2, Redis, RQ, HTTPX
• Static Analysis: Tree-sitter (tree-sitter-python, tree-sitter-typescript), Radon, NetworkX, GitPython
• Machine Learning: Scikit-learn, XGBoost, SHAP, MLflow, Pandas, NumPy, Joblib
• Frontend: React 18, TypeScript, Vite, Tailwind CSS, TanStack Query, React Flow (@xyflow/react), Recharts, Lucide Icons
• Infrastructure: Docker, Docker Compose, GitHub Actions CI, PostgreSQL 16, Redis 7

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7. Quality Assurance & Verification

All code adheres strictly to static analysis and automated verification standards:

# Run 92 backend tests across unit, integration, and security suites
pytest backend/tests/ -v

# Run Ruff linter and style checks
ruff check backend/ scripts/
ruff format --check backend/ scripts/

# Run MyPy strict static type checking
mypy --config-file backend/pyproject.toml backend/app

# Run Frontend unit tests and production build
npm --prefix frontend test
npm --prefix frontend run build

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8. Documentation Index

Comprehensive engineering guides are available in the docs/ directory:

• System Architecture Specification
• Dataset Collection & Chronological Splitting
• Feature Engineering & Mathematical Formulations
• Machine Learning Evaluation & Baseline Benchmarks
• Intelligent Test Selection & Optimization
• Security Architecture & Untrusted Code Isolation
• Deployment, Containerization & Operations

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9. Project Roadmap & Implementation Status

• Phase 1 — Project Foundations: Monorepo layout, FastAPI, SQLAlchemy 2.0, Alembic, React + Vite, Docker Compose, CI.
• Phase 2 — GitHub Integration: Webhook HMAC-SHA256 signature verification, Redis deduplication, GitHub API client.
• Phase 3 — Static Code Analysis Engine: Tree-sitter AST parsing, Radon cyclomatic complexity, NetworkX call graphs.
• Phase 4 — Historical Dataset Collection: Chronological splits without look-ahead leakage, 1,200 records across 4 repos.
• Phase 5 — Regression Risk Prediction: 25-feature vector, calibrated XGBoost, TreeSHAP local explainability.
• Phase 6 — Intelligent Test Selection: Coverage mapping, budgeted greedy marginal optimizer, benchmark suite.
• Phase 7 — REST API Endpoints: Analyses, features, dependencies, predictions, recommended tests, Prometheus metrics.
• Phase 8 — React Dashboard: Overview KPI cards, radial risk gauge, interactive React Flow dependency graph, test selection.
• Phase 9 — GitHub Checks Publisher: Collapsible Markdown summaries, check run formatting, CLI pytest generator.
• Phase 10 — Security Hardening: Timing-safe HMAC, path traversal fuzzing, corrupted diff resilience, token isolation.
• Phase 11 — End-to-End Testing: Webhook-to-dashboard workflow tests, failure injection, synchronizing superseded commits.
• Phase 12 — Empirical Benchmarks: 5-model comparison, PR-AUC, ROC-AUC, Brier score, ECE, test selection savings.
• Phase 13 — Containerization & Demo: Multi-stage Dockerfiles, zero-config bootstrap_demo.py script.
• Phase 14 — Comprehensive Documentation: In-depth technical specifications across all 7 architectural domains.