Tootfinder

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out:

Agentic Property-Based Testing: Finding Bugs Across the Python Ecosystem
Muhammad Maaz, Liam DeVoe, Zac Hatfield-Dodds, Nicholas Carlini
https://arxiv.org/abs/2510.09907 https:/…

Agentic Property-Based Testing: Finding Bugs Across the Python Ecosystem
Property-based testing (PBT) is a lightweight formal method, typically implemented as a randomized testing framework. Users specify the input domain for their test using combinators supplied by the PBT framework, and the expected properties or invariants as a unit-test function. The framework then searches for a counterexample, e.g. by generating inputs and calling the test function. In this work, we demonstrate an LLM-based agent which analyzes Python modules, infers function-specific and cros…

Computational Complexity in Property Testing
Renato Ferreira Pinto Jr., Diptaksho Palit, Sofya Raskhodnikova
https://arxiv.org/abs/2510.05927 https://arxiv…

Computational Complexity in Property Testing
We initiate a systematic study of the computational complexity of property testing, focusing on the relationship between query and time complexity. While traditional work in property testing has emphasized query complexity, relatively little is known about the computational hardness of property testers. Our goal is to chart the landscape of time-query interplay and develop tools for proving time complexity lower bounds. Our first contribution is a pair of time-query hierarchy theorems for prope…

How Ordos, a coal-rich northern Chinese city, has become a testing ground for self-driving trucks after turning into a ghost town due to 2012's coal price crash (Kinling Lo/Rest of World)
https://restofworld.org/2025/china-ordos-ghost-city-autonomous-vehicles/

China’s most infamous ghost town is now training ground for driverless trucks
The empty boulevards that once symbolized the mainland’s property bust now serve as testing grounds for hundreds of autonomous vehicles.

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
https://arxiv.org/abs/2510.10066 https://arx…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performance and resilience. Existing evaluations typically measure resistance to deobfuscation, leaving the critical question of whether obfuscators preserve program semantics unanswered. Incorrect transformations can silently alter functionality, compromise reliability, and erode security-undermining the very purpose of obfusc…

from my link log —
Property-based testing of OCaml 5’s runtime system.
https://janmidtgaard.dk/papers/Midtgaard:OLIVIERFEST25.pdf
saved 2025-10-06

Kathi asks ChatGPT "how to test a graph algo".
Repeats the query 6 months later…now it mentions PBT.
Huh! We ask it why it mentioned PBT. It cites back…
Our own papers and blog, and specifically mentions our own education research work on why graph algos are a great eg.
Moving the needle!

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Raphael Simon, Pieter Libin, Wim Mees
https://arxiv.org/abs/2509.20008 https://…

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investi…

Inferring Dynamic Physical Properties from Video Foundation Models
Guanqi Zhan, Xianzheng Ma, Weidi Xie, Andrew Zisserman
https://arxiv.org/abs/2510.02311 https://

Inferring Dynamic Physical Properties from Video Foundation Models
We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal information to be inferred: elasticity of a bouncing object, viscosity of a flowing liquid, and dynamic friction of an object sliding on a surface. To this end, we make the following contributions: (i) We collect a new video dataset for each physical property, consisting of synthetic training and testing splits, as well as a real split for real world …