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Static Analysis for AI-Generated Code

Building Production Analyzers to Detect, Diagnose, and Defend Against AI Code Quality Failures

This book is 100% completeLast updated on 2026-07-27
Steve Publications

AI-generated code can look flawless while hiding invented APIs, security gaps and subtle logic errors. This practical guide shows you how to build production-ready analyzers that catch what tests miss, from taint tracking and symbolic execution to custom rules for CI/CD and IDEs.

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About the Book

AI code generation has fundamentally changed the shape of software defects. Large language models produce code that looks correct but contains systematic failure modes: hallucinated APIs, prompt-induced vulnerabilities, architectural drift, and subtle logic errors that traditional testing cannot catch efficiently. This book teaches you how to build static analysis tools from first principles specifically designed to detect, diagnose, and defend against these AI-specific quality failures. You will learn compiler fundamentals, control flow and data flow analysis, taint tracking, symbolic execution, abstract interpretation, and rule engine design. Every chapter includes production-ready source code, architectural tradeoff discussions, and practical guidance for integrating analyzers into CI/CD pipelines and IDEs. By the end, you will understand not only how static analysis works but why each technique exists, when to use it, and how it applies to modern AI-assisted software development.

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Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

Every book is written, reviewed and maintained by experienced technology professionals, with contributions from our private technical community of more than 400 engineers and researchers from Ukraine, Belarus and Russia. We spend far more time validating technical accuracy and keeping our content up to date than generating text. We are always interested in working with experienced professionals who have deep expertise in a particular technology or domain. If you would like to publish a book with us or help review an existing manuscript, we'd love to hear from you. Send us a message describing your area of expertise. We are especially interested in niche technologies, specialized skills and emerging topics that are underrepresented in existing technical literature.

If you look through the contents of our books, you'll see practical examples, detailed explanations and material that is regularly updated. Our goal is to publish books that professionals can actually rely on, not low-effort AI-generated content. If you ever feel that one of our books does not meet that standard, Leanpub offers a 60-day money-back guarantee. Feel free to request a refund if you are not satisfied with your purchase.

Contents

Table of Contents

Building Production Analyzers to Detect, Diagnose, and Defend Against AI Code Quality Failures

Chapter 1: The Static Analysis Imperative in the AI Era

  1. How AI Code Generation Changed Software Engineering
  2. The New Defect Taxonomy: AI-Specific Failure Modes
  3. Why Dynamic Testing Is Not Enough
  4. Static Analysis as a Force Multiplier
  5. The Cost of Trusting Generated Code
  6. What This Book Will Teach You

Chapter 2: Compiler Fundamentals for the Static Analyst

  1. The Compiler Pipeline: From Source to Object
  2. Lexical Analysis and Tokenization
  3. Parsing and Grammar Design
  4. Error Recovery in Real Compilers
  5. Why You Need This Background as an Analyst

Chapter 3: Abstract Syntax Trees and Code Representation

  1. What an AST Is (and Is Not)
  2. Building an AST from Tokens
  3. The Visitor Pattern for Tree Traversal
  4. Implementing a Concrete AST Visitor
  5. Tree Transformations and Rewrites
  6. Comparing ASTs: Diffing Generated Code
  7. ASTs as the Foundation for Analysis

Chapter 4: Control Flow Graphs and Program Structure

  1. From AST to Control Flow Graph
  2. Basic Blocks and Dominators
  3. Reachability Analysis
  4. Detecting Dead Code in AI Output
  5. Loop Analysis and Termination Questions
  6. Exception Flow and Non-Local Control
  7. Why CFGs Matter for AI Code Analysis

Chapter 5: Data Flow Analysis and Symbol Resolution

  1. The Data Flow Problem
  2. Forward vs Backward Analysis
  3. Reaching Definitions
  4. Live Variable Analysis
  5. Symbol Tables and Scope Resolution
  6. Call Graph Construction
  7. Why Data Flow Matters for AI Code Analysis

Chapter 6: Type Systems and Type-Based Analysis

  1. What Types Are For
  2. Static vs Dynamic Typing for Analysis
  3. Type Inference Basics
  4. Detecting Type Confusion in Generated Code
  5. Null Safety and Option Types
  6. Generics, Polymorphism, and AI Pitfalls
  7. Why Type-Based Analysis Matters for AI Code

Chapter 7: Intermediate Representations and Cross-Language Analysis

  1. Why We Need Intermediate Representations
  2. LLVM IR as a Case Study
  3. Three-Address Code and SSA Form
  4. Tree-Sitter: Parsing Anything
  5. Building a Language-Agnostic Analyzer
  6. Cross-Language Call Graphs
  7. Why IRs Matter for AI Code Analysis

Chapter 8: Taint Analysis and Security Vulnerabilities

  1. The Taint Model
  2. Sources, Sinks, and Sanitizers
  3. Implementing a Taint Tracker
  4. Prompt-Induced Vulnerabilities
  5. Injection Attacks in Generated Code
  6. Insecure Default Patterns from AI
  7. Why Taint Analysis Matters for AI Code

Chapter 9: Symbolic Execution and Path-Sensitive Analysis

  1. Concrete vs Symbolic Execution
  2. Path Conditions and SMT Solvers
  3. Building a Basic Symbolic Executor
  4. Path Explosion and Mitigation
  5. When Symbolic Execution Is Worth It
  6. Concolic Testing for AI Code
  7. Why Symbolic Execution Matters for AI Code

Chapter 10: Abstract Interpretation and Sound Analysis

  1. The Abstract Interpretation Framework
  2. Lattices, Join, and Meet
  3. Widening and Narrowing
  4. Interval Analysis
  5. Pointer Analysis
  6. Tradeoffs: Precision vs Performance
  7. Why Abstract Interpretation Matters for AI Code

Chapter 11: Rule Engines and Custom Linting

  1. The Anatomy of a Static Analysis Rule
  2. Pattern Matching on ASTs
  3. Building a Rule Engine from Scratch
  4. Semgrep-Style Structural Search
  5. CodeQL Query Language Design
  6. Managing Thousands of Rules
  7. Why Rule Engines Matter for AI Code

Chapter 12: AI-Specific Code Smells and Detection Strategies

  1. Hallucinated APIs and Nonexistent Functions
  2. Inconsistent Coding Patterns and Style Drift
  3. Dead Code and Redundant Logic
  4. Hidden Dependencies and Missing Imports
  5. Context Window Artifacts
  6. Incomplete Implementations and TODO Sprawl
  7. Duplicated Logic Across Files
  8. Over-Engineering and Unnecessary Complexity
  9. Why Detecting AI Code Smells Matters

Chapter 13: Building a Production Static Analyzer

  1. Architectural Decisions for Scale
  2. Incremental Analysis and Caching
  3. Parallelization Strategies
  4. False Positive Management
  5. SARIF and Standardized Output
  6. Integration with CI/CD Pipelines
  7. Why Production Architecture Matters

Chapter 14: The Tooling Ecosystem

  1. Language-Specific Analyzers
  2. Query-Based Tools (CodeQL, Semgrep)
  3. IDE Integration Strategies
  4. Analyzing Large AI-Assisted Codebases
  5. Choosing Your Stack
  6. Building on Existing Infrastructure

Chapter 15: Defense in Depth — Integrating Static Analysis into AI Workflows

  1. Pre-Generation Guardrails
  2. Post-Generation Validation Pipelines
  3. Developer Feedback Loops
  4. Measuring AI Code Quality
  5. The Future of Static Analysis and AI
  6. A Practical Checklist

Conclusion

References

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