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如何分析Python代码以找出问题区域？

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问题：如何分析Python代码以找出问题区域？
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问题：如何分析Python代码以找出问题区域？

我有一个跨多个项目的大型资源库。我想生成一份有关源代码运行状况的报告，以确定需要解决的问题区域。

具体来说，我想找出循环复杂度高的例程，确定重复性，并可能进行一些类似于皮棉的静态分析，以发现可疑的（因而可能是错误的）构造。

我将如何构建这样的报告？

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I have a large source repository split across multiple projects. I would like to produce a report about the health of the source code, identifying problem areas that need to be addressed.

Specifically, I’d like to call out routines with a high cyclomatic complexity, identify repetition, and perhaps run some lint-like static analysis to spot suspicious (and thus likely erroneous) constructs.

How might I go about constructing such a report?

回答 0

为了测量圈复杂度，traceback.org提供了一个不错的工具。该页面还很好地概述了如何解释结果。

+1为pylint。它非常适合验证是否遵守编码标准（无论是PEP8还是您自己组织的变体），最终可以帮助降低循环复杂性。

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For measuring cyclomatic complexity, there’s a nice tool available at traceback.org. The page also gives a good overview of how to interpret the results.

+1 for pylint. It is great at verifying adherence to coding standards (be it PEP8 or your own organization’s variant), which can in the end help to reduce cyclomatic complexity.

回答 1

对于圈复杂度，可以使用radon：https : //github.com/rubik/radon

（使用pip安装它：pip install radon）

此外，它还具有以下功能：

  • 原始指标（包括SLOC，注释行，空白行等）
  • Halstead指标（所有指标）
  • 可维护性指数（在Visual Studio中使用的指数）
点击查看英文原文

For cyclomatic complexity you can use radon: https://github.com/rubik/radon

(Use pip to install it: pip install radon)

Additionally it also has these features:

  • raw metrics (these include SLOC, comment lines, blank lines, &c.)
  • Halstead metrics (all of them)
  • Maintainability Index (the one used in Visual Studio)

回答 2

对于静态分析，有pylint和pychecker。我个人使用pylint，因为它似乎比pychecker更全面。

对于圈复杂度，您可以尝试使用此perl程序，或者本文介绍了python程序来执行相同的操作

点击查看英文原文

For static analysis there is pylint and pychecker. Personally I use pylint as it seems to be more comprehensive than pychecker.

For cyclomatic complexity you can try this perl program, or this article which introduces a python program to do the same

回答 3

当您需要了解新项目时，Pycana就像魅力一样工作！

PyCAna（Python代码分析器）是一个简单的python代码分析器的名字，它在执行代码后创建类图。

看看它是如何工作的：http : //pycana.sourceforge.net/

输出：

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Pycana works like charm when you need to understand a new project!

PyCAna (Python Code Analyzer) is a fancy name for a simple code analyzer for python that creates a class diagram after executing your code.

See how it works: http://pycana.sourceforge.net/

output:

回答 4

感谢Pydev，您可以真正轻松地将pylint集成到Eclipse IDE中，并在每次保存修改后的文件时获得代码报告。

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Thanks to Pydev, you can integrate pylint in the Eclipse IDE really easily and get a code report each time you save a modified file.

回答 5

使用flake8，在一个工具中提供pep8，pyflakes和循环复杂性分析

点击查看英文原文

Use flake8, which provides pep8, pyflakes, and cyclomatic complexity analysis in one tool

回答 6

有一个名为CloneDigger的工具 ，可以帮助您找到类似的代码片段。

点击查看英文原文

There is a tool called CloneDigger that helps you find similar code snippets.

回答 7

为了检查圈复杂度，当然有mccabe包装。

安装：

$ pip install --upgrade mccabe

用法：

$ python -m mccabe --min=6 path/to/myfile.py

请注意上面的阈值6。根据这个答案，分数> 5可能应该简化。

输出示例--min=3：

68:1: 'Fetcher.fetch' 3
48:1: 'Fetcher._read_dom_tag' 3
103:1: 'main' 3

也可以通过pylint-mccabe或pytest-mccabe等使用它。

点击查看英文原文

For checking cyclomatic complexity, there is of course the mccabe package.

Installation:

$ pip install --upgrade mccabe

Usage:

$ python -m mccabe --min=6 path/to/myfile.py

Note the threshold of 6 above. Per this answer, scores >5 probably should be simplified.

Sample output with --min=3:

68:1: 'Fetcher.fetch' 3
48:1: 'Fetcher._read_dom_tag' 3
103:1: 'main' 3

It can optionally also be used via pylint-mccabe or pytest-mccabe, etc.