As developers, there always comes a time when we find a bug in production and wonder how it passed all our quality checks. The truth is that we can never be sure our code is bug free. We can only choose the tools and workflows which will find the most bugs without slowing us down too much. SonarQube, SonarLint and SonarCloud are such tools. We used SonarCloud during our recent bug report campaign, which focused on popular projects such as , , , and . The campaign result was quite interesting, since it shows the kind of bugs we can find in a Python project even when its development workflow includes every best practice: code reviews, high test coverage, and the use of one or more linters (flake8, pylint, ...). tensorflow numpy salt sentry biopython Let's go over a few Bugs we found with SonarCloud and see why it is able to detect them when popular linters don't . Reference to an undefined variable SonarCloud can detect buggy references to undefined variables when the variables are defined in another branch. It uses a to deduce that the definition of the variable will never occur before the buggy reference. if-else Control Flow Graph Unreachable code Detecting dead code is easy when it's just after a or a statement. It's a little harder when the is conditional. We use a control flow graph to detect cases where multiple branches exit just before reaching a statement. return raise return Wrong fields in formatted strings It is quite common to reference the wrong field name or index during string formatting. Pylint and Flake8 have rules detecting this problem with string literals, but they miss bugs when the format string is in a variable. Type errors SonarCloud has a type inference engine, which enables it to detect advanced type errors. It uses every bit of information it can find to deduce variable type, including stubs, assignments, and your type annotations.. At the same time, it won't complain if you don't use type annotations, and it's designed to avoid False Positives. Typeshed In this example, control flow analysis is what allows it to understand that is a tuple because it is assigned when is a . The algorithm is able to ignore the later assignments to . state_shape output_shape[1:] output_shape tuple list output_shape Now let's look at some more specific examples. Wrong argument type SonarCloud uses stubs to know the types expected by builtins functions. So here it raises an issue because you get a if you call the builtin on an integer. Typeshed TypeError len Comparisons that don't make sense SonarCloud has many rules detecting code which doesn't make sense. Comparing incompatible types with will never fail, but it will always return False, or True if you use . Here we can see an issue because returns a tuple. == != platform.architecture() Return values from functions without side effects should not be ignored Some function calls have no side effect, i.e. they won't change anything by themselves and their only purpose is to return a value. Thus  there is always a bug when their result is not used. SonarCloud knows an extensive list of such functions. In this example the two strings are not concatenated; the method is called on the second string and the result is discarded, so the value of is "Make sure that your dataset can generate at least ". format warning_msg Unraised exceptions When we review code we usually look at classes, variables and other meaningful symbols and we forget to check little details, such as "is there a raise keyword before my exception". SonarCloud analyzes your whole project to extract type hierarchies. Thus it detects when custom exceptions are discarded, not just the builtin ones. Flake8 is great but not enough Some of the things [SonarCloud] spots are impressive (probably driven by some introspection and/or type inference), not just the simple pattern matching that I am used to in most of the flake8 ecosystem. - Peter J. A. Cock - maintainer of BioPython ( original post here ) This is one of the nice pieces of feedback we received during our bug report campaign. ( !). There's more All the projects we examined use one or more linters, such as Flake8, which is very popular, and is often included in CI workflows. There are very good reasons for Flake8's broad use: it focuses on uncontroversial rules that generate few false positives It checks pep8 style It is fast , and have the same philosophy about speed and false positives. All three target developers, which means that . In addition, SonarCloud and SonarQube can both import Flake8 issues. But most importantly: SonarLint SonarCloud SonarQube we work hard to keep "noise" to a minimum they detect a broader range of issues. Not just style and pattern matching, but a full range of . bugs, code smells and vulnerabilities they help you focus on achieving high quality in recent changes (i.e. ) rather than distracting you with small flaws in old code Clean as You Code they support all the languages in your project. For example if you've got JavaScript or TypeScript alongside your Python, it will be analyzed simultaneously, with no more setup or infrastructure. You can use SonarCloud for free on any open source project and get started with just a few clicks. is also free for unlimited on-premises use. Don't hesitate to share your feedback, good or bad, . It helps us improve our tools everyday. SonarQube Community Edition in our community forum Previously published at https://blog.sonarsource.com/sonarcloud-finds-bugs-in-high-quality-python-projects

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