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Chapter 15: Modeling Code

  • Overview of network graphs and their relevance in software engineering.
  • Why network graphs provide unique insights into code structure and behavior.
  • Benefits of graph-based analysis for developers (e.g., debugging, performance optimization, and testing).

Core Concepts of Code Graphs

  • Nodes and Edges: Representing Code Elements
    • Nodes as functions, classes, or modules.
    • Edges as relationships (e.g., calls, dependencies, data flow).
  • Directed vs. Undirected Graphs
    • Understanding the directionality of relationships in code.
  • Weighted Edges
    • Representing metrics like frequency of calls, execution time, or code complexity.

Call Graph Modeling

Software can be modeled as a call graph. Each software function can be modeled as a node in a graph. If a function calls another function, an edge is created between those functions. If function A calls function B we can say that A may depend on function B. If function B has a bug, then we might determine that function A also has this bug as well as all other functions that depend on function A.

  • Definition and Purpose
    • A graph where nodes represent functions and edges represent function calls.
  • Applications
    • Identifying dead code or unused functions.
    • Optimizing recursive or heavily called functions.
  • Creating Call Graphs
    • Tools for generating call graphs from codebases.
    • Examples of languages or frameworks (e.g., Python's pycallgraph or Java's javacg).
  • Analyzing Call Graphs
    • Metrics: Depth, breadth, and connectivity of the graph.
    • Detecting circular dependencies.
  • Case Study: Debugging with Call Graphs
    • Walkthrough of using a call graph to resolve a real-world problem.

Text Coverage Graphs**

  • Definition and Importance
    • Modeling relationships between text (e.g., comments, documentation) and code elements.
  • Applications
    • Tracking which parts of the code are well-documented.
    • Ensuring test cases cover all branches and lines of code.
  • Building Text Coverage Graphs
    • Representing links between test cases, functions, and documentation.
    • Tools and frameworks for text-to-code mapping.
  • Analyzing Coverage
    • Identifying gaps in documentation or testing.
    • Strategies to improve coverage (e.g., automated tools for generating missing links).

Integrating Multiple Graphs

  • Combining call graphs with text coverage graphs.
  • Visualizing interdependencies between code execution and documentation/testing.
  • Examples of hybrid graphs for holistic code analysis.

Graph-Based Code Quality Metrics

  • Key Metrics
    • Cyclomatic complexity.
    • Code churn and its representation in graphs.
    • Hotspots: Functions or modules with high connectivity or execution frequency.
  • Using Metrics for Refactoring
    • Prioritizing areas for improvement based on graph insights.

Tools for Graph-Based Code Analysis

  • Overview of popular tools and libraries for building and analyzing code graphs.
    • Static analysis tools (e.g., Graphviz, Doxygen).
    • Dynamic analysis tools (e.g., Jaeger for distributed systems).
  • Integrating graph analysis into CI/CD pipelines.

Case Studies

  • Example 1: Using Call Graphs for Performance Optimization.
  • Example 2: Ensuring Comprehensive Test Coverage with Text Coverage Graphs.
  • Example 3: Visualizing Large Legacy Codebases with Hybrid Graphs.

Future Directions

  • Advanced use cases: Predictive modeling and AI-driven refactoring using code graphs.
  • Real-time visualization of code execution graphs in debugging tools.
  • Collaboration and version control integration with graph modeling.

Hands-On Exercises

  • Exercise 1: Generate and analyze a call graph for a small project.
  • Exercise 2: Create a text coverage graph linking test cases to code functions.
  • Exercise 3: Combine call graphs and text coverage graphs for a holistic analysis.
  • Exercise 4: Use graph metrics to identify and refactor a code hotspot.

Conclusion**

  • Recap of the value of modeling code with network graphs.
  • Encouragement to integrate graph-based analysis into everyday development practices.