pynguin
spaCy
pynguin | spaCy | |
---|---|---|
11 | 107 | |
1,200 | 28,789 | |
1.0% | 0.8% | |
8.2 | 9.2 | |
15 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
pynguin
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There is framework for everything.
https://swagger.io/specification/ https://github.com/se2p/pynguin
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Supposed to create tests for a massive project, how should I go about it?
I would use black to reformat this, then, if you can't refactor/rewrite (which is a lot of work!) I would try automated test generation via something like pynguin or fuzzing. I mean … this is not going to be a reliable solution anyways if the codebase is like that. So I would go in a direction that I find interesting to learn about and that could be helpful for the project. That would be generating tests and doing fuzzing. In the end you should run some linters anyways so that you can justify your results and show that the task is not in the scope of an internship and needs extensive refactoring.
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Klara: Python automatic test generations and static analysis library
The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Automated White-box test generation software: * https://github.com/EMResearch/EvoMaster -- for integration tests. * https://github.com/se2p/pynguin, https://pynguin.readthedocs.io/en/latest/user/quickstart.html -- unit test generation for python
- se2p/pynguin Pynguin, the PYthoN General UnIt test geNerator, is a tool that allows developers to generate unit tests automatically.
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Hacker News top posts: Jun 1, 2021
Pynguin – Generate Python unit tests automatically\ (60 comments)
- Pynguin – Generate Python unit tests automatically
- Pynguin – Allow developers to generate Python unit tests automatically
spaCy
- How I discovered Named Entity Recognition while trying to remove gibberish from a string.
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
What are some alternatives?
CrossHair - An analysis tool for Python that blurs the line between testing and type systems.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
EvoMaster - The first open-source AI-driven tool for automatically generating system-level test cases (also known as fuzzing) for web/enterprise applications. Currently targeting whitebox and blackbox testing of Web APIs, like REST, GraphQL and RPC (e.g., gRPC and Thrift).
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
icontract-hypothesis - Combine contracts and automatic testing.
NLTK - NLTK Source
klara - Automatic test case generation for python and static analysis library
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
methods2test - methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories
polyglot - Multilingual text (NLP) processing toolkit
code - Example application code for the python architecture book
textacy - NLP, before and after spaCy