Tootfinder

A researcher finds that X is testing a feature that lets users create or join communities focused on "adult content" or other "not safe for work" material (Kurt Wagner/Bloomberg)
https://www.bloomberg.com/news/articles/20

Elon Musk’s X Is Testing ‘Adult Content’ Groups for Users
X, the social network formerly known as Twitter, is testing a feature that lets users create or join communities focused on “adult content” or other “not safe for work” material.

This is a legit clever way to test a zillion closely-related test cases. And not just for regexes as shown here.
#PHP

Testing Patterns - stitcher.io
A blog about modern PHP, the web, and programming in general. Follow my newsletter and YouTube channel as well.

This https://arxiv.org/abs/2305.15698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Rethinking Diversity in Deep Neural Network Testing
Motivated by the success of traditional software testing, numerous diversity measures have been proposed for testing deep neural networks (DNNs). In this study, we propose a shift in perspective, advocating for the consideration of DNN testing as directed testing problems rather than diversity-based testing tasks. We note that the objective of testing DNNs is specific and well-defined: identifying inputs that lead to misclassifications. Consequently, a more precise testing approach is to priori…

"What’s in a Name?"
A useful reminder to put some thought into function names and such in software.
(not sure why it is in the Google *Testing* Blog, but still useful content)
https://testing.googleblog.com/2024/03/whats-in-name.html

What’s in a Name?
This is another post in our Code Health series. A version of this post originally appeared in Google bathrooms worldwide as a Google Tes...

The text of this post is flat-out wrong (sorry, @…, ht @…):

StartsWithABang (@startswithabang@astrodon.social)
Attached: 1 image Colleges should include standardized testing in admissions. Why? Because standardized testing is the one place where even the most advantaged rich kid can't outcompete a more talented poor kid. https://bigthink.com/starts-with-a-bang/colleges-standardized-testing-admissions/

"As a medical doctor...specializing in addiction medicine, I have seen patients overcome substance use issues. Very few of them did so without stable housing. Homelessness increases the chaos in someone’s life, and the more chaos there is, the harder it is to overcome a substance use problem."
https://

Opinion | Drug testing welfare recipients is a dangerous idea
San Francisco Mayor London Breed wants mandatory drug testing for welfare recipients. It would likely create even more homelessness among the most vulnerable and strain our already overloaded health care system.

IBM plans to expand its semiconductor packaging and testing plant in Bromont, Quebec, with more than CA$1B in investments over the next five years (Mathieu Dion/Bloomberg)
https://www.bloomberg.com/news/articles/202…

IBM to Expand Canadian Semiconductor Packaging and Testing Plant
International Business Machines Corp. will expand its Canadian semiconductor packaging and testing plant with more than C$1 billion ($730 million) in investments over the next five years.

The depth of testing behind sqlite is insane: https://www.sqlite.org/testing.html

Statistical testing of random number generators and their improvement using randomness extraction
Cameron Foreman, Richie Yeung, Florian J. Curchod
https://arxiv.org/abs/2403.18716

Statistical testing of random number generators and their improvement using randomness extraction
Random number generators (RNGs) are notoriously hard to build and test, especially in a cryptographic setting. Although one cannot conclusively determine the quality of an RNG by testing the statistical properties of its output alone, running numerical tests is both a powerful verification tool and the only universally applicable method. In this work, we present and make available a comprehensive statistical testing environment (STE) that is based on existing statistical test suites. The STE ca…

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
Han Wang, Sijia Yu, Chunyang Chen, Burak Turhan, Xiaodong Zhu
https://arxiv.org/abs/2402.16546

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness. However, it is unclear whether DL projects, as software systems, are tested thoroughly or functionally correct when there is a need to treat and test them like other software systems. Therefore, we empirically study the unit tests in open-source DL projects, analyzing 9,129 projects from GitHub. We find that: 1) unit tested DL projects have positive correlation …