Static Analysis Security: Code Quality Metric Essentials Quiz

Explore key code quality metrics used in static analysis for security testing, including definitions, practical significance, and interpretation of results. This quiz helps professionals understand how static code analysis supports secure software development and pinpoints common metric pitfalls.

1. Cyclomatic Complexity in Security Testing

   Which aspect does cyclomatic complexity specifically measure in static code analysis for security testing, for example in a function with multiple loops and branches?

   1. The number of independent execution paths in the code segment
   2. The total number of variables declared in the code
   3. The amount of memory allocated at runtime
   4. The size of the codebase in lines of code

   Explanation: Cyclomatic complexity measures the number of independent execution paths through a program segment, making it relevant for assessing code's testability and potential vulnerabilities. Counting variables or memory is unrelated to this metric. Code size in lines does not consider branching and logic flow, whereas cyclomatic complexity directly reflects code structure’s complexity and potential risk areas.

2. Identifying a Code Smell

   In static analysis, which metric might help identify a 'God Object' or 'God Class'—for example, a class that performs too many functions and interacts with numerous modules?

   1. Coupling between objects
   2. Stack pointer address
   3. Heap allocation rate
   4. Code indentation level

   Explanation: Coupling between objects measures the degree of dependency among modules or classes, which can reveal when a single class is overly dependent on or interacts with many others—a symptom of a God Class. Stack pointer address and heap allocation rate pertain to runtime, not static structural analysis. Code indentation is related to style and readability, not code quality regarding object interactions.

3. False Positives from Static Metrics

   When analyzing code with a static tool that reports high 'lines of code' but shows only a few vulnerabilities, what issue may arise from relying solely on this metric for assessing security risk?

   1. Assuming more lines always mean higher risk, which may not be true
   2. Forgetting to compile the code before analysis
   3. Automatically fixing all vulnerabilities found
   4. Reducing code indentation to hide issues

   Explanation: Relying solely on lines of code can lead to false assumptions about risk, as more code does not inherently mean more vulnerabilities. Not compiling before analysis is unrelated in static analysis since compilation is not required. Automatically fixing vulnerabilities doesn't address reliance on metrics, and changing indentation only affects readability, not actual code risk.

4. Interpreting Code Duplication Metrics

   Why is a high code duplication metric a concern in static security analysis, using an example where code blocks are copy-pasted across multiple files?

   1. It increases the surface area for potential security flaws by multiplying similar vulnerable code
   2. It always results in faster program execution
   3. It forces all code to be rewritten in a different language
   4. It ensures that all bugs are completely eliminated

   Explanation: High code duplication means similar code is repeated across files, so a vulnerability in one block may exist in many places, increasing the risk and remediation effort. Duplication does not guarantee faster execution, nor does it require a rewrite in another language. Rather than eliminating bugs, it makes bugs more widespread and harder to fix comprehensively.

5. Importance of Comment Density

   How does a low comment density metric impact static code analysis for secure programming practices, for example in a module with complex algorithms?

   1. It makes the code harder to understand and maintain, increasing the chance of introducing or missing security issues
   2. It automatically makes the code run faster at runtime
   3. It determines the programming language being used
   4. It forces all functions to be private by default

   Explanation: Low comment density means there is little explanatory text to guide reviewers and maintainers, making it easier for mistakes or vulnerabilities to go unnoticed. Comment density does not affect code execution speed or language choice, nor does it enforce code visibility rules like making functions private. Adequate comments support robust security by aiding in code reviews and understanding complex logic.