What form of static analysis can be used on dependencies to simulate the code execution? For example a Pydantic model class defined at runtime is 'built' (its core schema is collected from model fields from the class type information) and when an instance is subsequently instantiated at runtime, it invokes the validators on the schema.
How would all of this be traced in static analysis? Do not leave any details vague
To perform static analysis that simulates code execution, particularly for complex scenarios like Pydantic model class definition and instantiation, we can use a technique called symbolic execution combined with type inference. This approach allows us to analyze the code without actually running it, while still capturing the effects of runtime behaviors. Let's break down how this would work for your Pydantic example:
Control Flow Graph (CFG) Construction: First, we build a control flow graph of the entire codebase, including the Pydantic library code. This graph represents all possible execution paths.
Symbolic Execution: We use symbolic execution to traverse the CFG. Instead of concrete values, we use symbolic values to represent inputs and intermediate results.
Type Inference: As we symbolically execute the code, we perform type inference to understand the structure of classes and objects.
Class Metadata Collection: When we encounter a Pydantic model class definition, we symbolically execute the metaclass logic (which is what Pydantic uses to process model definitions). This involves:
a. Collecting field definitions
b. Processing field types and any type annotations
c. Registering validators and other field-specific behaviors
Schema Building Simulation: We simulate the schema building process by:
a. Analyzing the collected metadata
b. Creating symbolic representations of the schema
c. Linking validators to their respective fields
Instance Creation Simulation: When we encounter code that creates an instance of the Pydantic model, we:
a. Create a symbolic instance of the model
b. Simulate the initialization process
c. For each field, we create symbolic values and apply the registered validators
Validator Execution Simulation: For each validator, we:
a. Symbolically execute the validator function
b. Track constraints on the symbolic values based on the validator logic
c. Handle any potential exceptions or error conditions
State Tracking: Throughout this process, we maintain a symbolic state that represents:
a. The current point in the CFG
b. The set of constraints on all symbolic values
c. The structure of objects and their fields
Path Exploration: We explore multiple paths through the CFG, considering different branches based on symbolic conditions.
Abstract Interpretation: We use abstract interpretation techniques to reason about the behavior of complex operations, especially those involving external libraries or system calls.
Alias Analysis: We perform alias analysis to track references and understand when multiple variables might point to the same object.
Effect Analysis: We track side effects of method calls and function invocations, especially those that might modify the state of objects or the global environment.
Constraint Solving: At key points in the analysis (e.g., when checking if a certain code path is reachable), we use constraint solvers to determine if the accumulated constraints are satisfiable.
Partial Evaluation: For parts of the code that can be safely evaluated at analysis time, we use partial evaluation to simplify the symbolic execution.
Model-specific Optimizations: We can implement specialized handling for known libraries like Pydantic, encoding our understanding of their internal behavior into the analysis engine.
Result Aggregation: As we explore different paths, we aggregate results to build a comprehensive understanding of possible behaviors, including:
a. Potential runtime errors
b. Data flow patterns
c. Object shapes and constraints
Scalability Considerations: To make this analysis feasible for large codebases:
a. We use summarization techniques to create abstract models of function behavior
b. We employ lazy evaluation strategies to avoid exploring irrelevant code paths
c. We use caching and memoization to avoid redundant analysis
Handling Dynamic Features: For highly dynamic features (e.g., eval, exec, or dynamic imports), we either:
a. Provide conservative approximations of their behavior
b. Allow the user to provide annotations or models of their expected behavior
This comprehensive approach allows us to simulate the runtime behavior of Pydantic models (and similar dynamic constructs) with a high degree of fidelity, capturing complex interactions between type definitions, schema building, and validation logic, all without actually executing the code.
The result is a static analysis that can predict potential runtime behaviors, type inconsistencies, and possible errors, while also providing insights into data flow and object shapes throughout the program.