How to use Python regular expressions for code complexity analysis

As software development continues to advance, code quality becomes more and more important. Code complexity analysis is one of the key links. Code complexity analysis can help developers discover potential problems, avoid loopholes and errors in the code, and improve the maintainability and readability of the code. This article will introduce how to use Python regular expressions for code complexity analysis.

1. What is code complexity analysis

Code complexity is an indicator to measure the difficulty of code, including two aspects: the complexity of the code execution path and the code structure on the complexity. The complexity of an execution path is measured by the number of basic paths, which are simple paths in the program that do not contain loops. The complexity of the code structure depends on the number of nested levels of code blocks, control structures, and functions. These indicators can be used to quantitatively measure the complexity of a software system for better maintenance and testing.

2. Use regular expressions to analyze code complexity

Regular expression is an expression used to match strings, usually used to search, replace and Split text. In code complexity analysis, we can use regular expressions to search for specific patterns in the code to count the number of nested levels of control structures and functions in the code, as well as the number of execution paths.

2.1 Search for control structures and functions

In Python, we can use regular expressions to search the beginning and end of control structures and functions such as if, for, while, and def in the code. Here is a simple regular expression example to match if statements in Python code:

if .*:

This regular expression matches any line of code that starts with if and ends with a colon. In this way, we can search for all if statements, for loops, and while loops in the code and count their nesting levels.

2.2 Calculate the number of nesting levels

The number of nesting levels refers to the number of levels of one control structure or function within another control structure or function. In order to count the number of nesting levels, we can use the stack structure in Python to save the code blocks and functions being processed. When we encounter a new control structure or function, we push it onto the stack and pop it after processing. The remaining elements in the stack represent the number of nesting levels. Here is a sample code:

import re

def parse_code(code):
    stack = []
    depth = 0

    for line in code.split("
"):
        if re.match(".*:s*$", line):
            stack.append("block")
            depth += 1
        elif re.match("def.*:", line):
            stack.append("function")
            depth += 1
        elif re.match(".*s(if|else|elif|for|while)s.*:", line):
            depth += 1
        while stack and stack[-1] != "block":
            stack.pop()
            depth -= 1
        if stack:
            print("{:>2}: {}".format(depth, line.strip()))

        if re.match("^s*$", line):
            while stack and stack[-1] != "block":
                stack.pop()
                depth -= 1
    return depth

This function splits the code by lines and then uses regular expressions to search for the if, else, elif, for and while keywords as well as function, def and colon. When a code block or function definition is encountered, it is pushed onto the stack. We then find the block of code or function we are working on at the top of the stack and calculate the depth as needed.

2.3 Calculate the number of basic paths

Basic paths refer to simple paths that do not contain loops in the program. In order to count the number of basic paths, we can use code coverage analysis techniques to traverse all paths of the program and count their number. The following is a sample code:

import re

def count_paths(code):
    paths = []
    visited = set()

    def walk(path):
        if path[-1] in visited:
            return

        visited.add(path[-1])

        if re.match(".*:s*$", path[-1]):
            paths.append(list(path))

        for i, line in enumerate(code.split("
")):
            if line == path[-1]:
                for j in range(i+1, len(code.split("
"))):
                    if line in code.split("
")[j]:
                        walk(path + [code.split("
")[j]])

    for i, line in enumerate(code.split("
")):
        if re.match(".*:s*$", line):
            walk([line])
            break

    return len(paths)

This function uses a recursive method to traverse all paths of lines in the code and only records simple paths that do not contain loops.

3. Summary

Code complexity is a crucial parameter in software development. By calculating complexity, we can better understand the structure and difficulty of the program, and can Help developers find possible vulnerabilities and errors in their code. This article introduces how to use Python regular expressions for code complexity analysis, including searching for control structures and functions, calculating the number of nesting levels, and calculating the number of basic paths. I hope this article can help readers better understand and analyze the complexity of software code and improve the maintainability and readability of the code.

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