PMD
warehouse
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PMD | warehouse | |
---|---|---|
21 | 274 | |
4,654 | 3,465 | |
1.4% | 0.7% | |
9.9 | 9.7 | |
1 day ago | 1 day ago | |
Java | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PMD
- PMD 7 Is Here
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Amazon CodeGuru Reviewer: already time for retirement?
While the security findings can be pretty elaborate and helpful, the code quality and performance focused findings are not that impressive and can often be detected by more basic or powerful tools like SonarQube (paying) or PMD (free). To see what I mean you can have a look at the list of Java code quality detectors, which is pretty short and contains a lot of simple findings like:
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Code Review for Flows
Also saw this convo has a couple years worth of ideas going on … https://github.com/pmd/pmd/issues/3413
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Custom Gradle Plugin for Unified Static Code Analysis
PMD and Checkstyle are static analysis tools that check your code on each project build. Gradle allows to apply them easily.
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Spring Boot – Black Box Testing
The generated classes should be put into .gitignore. Otherwise, if you have Checkstyle, PMD, or SonarQube in your project, then generated classes can violate some rules. Besides, if you don't put them into .gitignore, then each pull request might become huge due to the fact that even a slightest fix can lead to lots of changes in the generated classes.
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After Java tutorials, now what???
- PMD Static Code Analysis tool: https://pmd.github.io/
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Ask HN: What is a modern Java environment?
PMD, Spotbugs, Nullaway: Java linting/static analysis (https://pmd.github.io, https://spotbugs.github.io, https://github.com/uber/NullAway)
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Code smell plugin
PMD, and checkstyle as well.
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Writing Clean and Consistent Code with Static Analysis using PMD and Apex
Open up the config/ruleset.xml file, and you’ll find an XML document that lists several rules. These rules map to the issues which PMD will report on. Believe it or not, there are hundreds of Apex rules, and you can find the full set at the PMD repo. You have complete control over which rules to enable. Typically, you’d determine which ones are important by agreeing with your teammates on the ones that matter most. After all, their code will be statically analyzed, too!
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Is there a tool to track CVEs for the software that we use?
While at it you could also point them to static code analyzers such as error_prone, spotbugs and pmd (use all 3 at once - they complement each other in detecting different issues).
warehouse
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
python3 -m pip install \ --trusted-host test.pypi.org --trusted-host test-files.pythonhosted.org \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple/ \ piper_whistle==$(python3 -m src.piper_whistle.version)
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Pickling Python in the Cloud via WebAssembly
In my experience so far, I can use a vast amount of the Python Standard Library to build Wasm-powered serverless applications. The caveat I currently understand is that Python’s implementation of TCP and UDP sockets, as well as Python libraries that use threads, processes, and signal handling behind the scenes, will not compile to Wasm. It is worth noting that a similar caveat exists with libraries that I find on The Python Package Index (PyPI) site. While these caveats might limit what can be compiled to Wasm, there are still a ton of extremely powerful libraries to leverage.
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Introducing Flama for Robust Machine Learning APIs
We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.
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PyPI Packaging
From there, I needed to learn a bit about PyPi or Python Package Index, which is the home for all the wonderful packages that you know if you have ever run the handy pip install command. PyPi has a pretty quick and easy onboarding, which requires a secured account be created and, for the purposes of submitting packages from CLI, an API token be generated. This can be done in your PyPi profile. Once logg just navigate to https://pypi.org/manage/account/ and scroll down to the API tokens section. Click “Add Token” and follow the few steps to generate an API token which is your access point to uploading packages. With all this in place, I was able to use twine to handle the package upload. First I needed to install twine, again as simple as pip install twine. In order for twine to access my API token during the package upload process, it needed to read it from .pypirc file that contains the token info. For some that file may exist already, for me I was required to create it. Working in windows I simply used a text editor to create it in my home user directory ($HOME/.pypirc). The file contents had a TOML like format looked like this:
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Releasing my Python Project
I have published the package to Python Package Index, commonly called PyPi, and in this post, I'll be sharing the steps I had to follow in the process.
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Publishing my open source project to PyPI!
Register at PyPI.org
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Show HN: I mirrored all the code from PyPI to GitHub
According to the stats on the original link, there are over 25,000 identified secret ids/keys/tokens in the data. And it looks like that's just identifiable secrets, e.g. "Google API Keys" that I'm guessing are identifiable because they have a specific pattern, and may be missing other secrets that use less recognizable patterns.
I mean, sure, compared to the 478,876 Projects claimed on https://pypi.org/, that's a pretty small minority. On the other hand, I'd guess a many Python packages don't use these particular services, or even need to connect to a remote service at all, so the area for this class of mistake should be even smaller.
And mistakes do happen, but that's a pretty big thing to miss if you are knowingly publishing your code with the expectation other people will be reading it.
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Pezzo v0.5 - Dashboards, Caching, Python Client, and More!
PyPi package
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Modifying keywords in python package
Does pypi.org display the Union of all keywords, the keywords of the most recent release, the keywords of the first release or some other weird combination like the intersection?
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PyPI Requires 2FA for New User Registrations
https://peps.python.org/pep-0458/
Here's the in-progress roadmap: https://github.com/pypi/warehouse/issues/10672
If there's particular issues you believe you could pick off to help achieve the goal, much appreciated!
What are some alternatives?
Spotbugs - SpotBugs is FindBugs' successor. A tool for static analysis to look for bugs in Java code.
devpi
SonarQube - Continuous Inspection
bandersnatch
Error Prone - Catch common Java mistakes as compile-time errors
localshop - local pypi server (custom packages and auto-mirroring of pypi)
Checkstyle - Checkstyle is a development tool to help programmers write Java code that adheres to a coding standard. By default it supports the Google Java Style Guide and Sun Code Conventions, but is highly configurable. It can be invoked with an ANT task and a command line program.
Poe the Poet - A task runner that works well with poetry.
infer - A static analyzer for Java, C, C++, and Objective-C
scribd-downloader
SonarJava - :coffee: SonarSource Static Analyzer for Java Code Quality and Security
Python Packages Project Generator - 🚀 Your next Python package needs a bleeding-edge project structure.