Another viable method to identify potential errors and bugs in your concurrent programs is to perform static code analysis. This method looks for patterns in the code itself, as opposed to executing some (or all) parts of the code. In other words, static code analysis inspects a program by visually looking at its structure, the use of variables and instructions, and how different parts of the program interact with each other.

The main advantage of using static code analysis is that we are not relying on just the execution of our programs and the results produced during that process (in other words, dynamic testing) to determine whether the programs are correctly designed. This method can detect errors and bugs that do not manifest themselves (easily, or at all) in implemented tests. For this reason, static code analysis should be combined with other testing methods, such as unit testing, to create a comprehensive testing process.

Static code analysis is often used to identify subtle errors or bugs, such as unused variables, empty catch blocks, or even unnecessary object creation. In terms of concurrent programming, the method can be used to analyze synchronization techniques used in a program. Specifically, static code analysis can look for the atomicity of shared resources in a program, then reveal any uncoordinated usage of non-atomic resources that could produce detrimental race conditions.

Various tools are available to facilitate static code analysis for Python programs, with one of the more common ones being PMD (https://github.com/pmd/pmd). With that said, the specific use of these tools is beyond the scope of this book, and we will not go into them further.