如何分析Python代码以识别问题区域？ [英] How can I analyze Python code to identify problematic areas?

问题描述

我有一个大型的源代码仓库，分布在多个项目中。我想生成一份有关源代码运行状况的报告，确定需要解决的问题区域。

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.

特别是，我想用高度复杂的循环，确定重复，并可能进行类似棉绒的静态分析以发现可疑（因而可能是错误的）构造。

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.

我应该如何构造这样的构造

How might I go about constructing such a report?

推荐答案

要测量圈复杂度，在 traceback.org 。该页面还很好地概述了如何解释结果。

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表示圆筒。它非常适合验证是否遵守编码标准（无论是 PEP8 还是您自己的组织的变体），最终可以帮助降低圈复杂度。

+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.

这篇关于如何分析Python代码以识别问题区域？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！