Tootfinder

Testing Depth First Search Numbering
Artur Czumaj, Christian Sohler, Stefan Walzer
https://arxiv.org/abs/2509.05132 https://arxiv.org/pdf/2509.05132…

Testing Depth First Search Numbering
Property Testing is a formal framework to study the computational power and complexity of sampling from combinatorial objects. A central goal in standard graph property testing is to understand which graph properties are testable with sublinear query complexity. Here, a graph property P is testable with a sublinear query complexity if there is an algorithm that makes a sublinear number of queries to the input graph and accepts with probability at least 2/3, if the graph has property P, and reje…

Largevars: An R Package for Testing Large VARs for the Presence of Cointegration
Anna Bykhovskaya, Vadim Gorin, Eszter Kiss
https://arxiv.org/abs/2509.06295 https://

Largevars: An R Package for Testing Large VARs for the Presence of Cointegration
Cointegration is a property of multivariate time series that determines whether its non-stationary, growing components have a stationary linear combination. Largevars R package conducts a cointegration test for high-dimensional vector autoregressions of order k based on the large N, T asymptotics of Bykhovskaya and Gorin (2022, 2025). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy_1 …

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:

Replaced article(s) found for cs.CG. https://arxiv.org/list/cs.CG/new
[1/1]:
- Property Testing of Curve Similarity
Peyman Afshani, Maike Buchin, Anne Driemel, Marena Richter, Sampson Wong

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein, Todd Millstein, Benjamin Pierce, Guy Van den Broeck
https://arxiv.org/abs/2508.14394

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is via generators that describe how to sample test inputs through random choices. To achieve a good distribution over test inputs, users must tune their generators, i.e., decide on the weights of these individual random choices. Unfortunately, it is very difficult to understand how to choose individual generator weights…

Use Property-Based Testing to Bridge LLM Code Generation and Validation
Lehan He, Zeren Chen, Zhe Zhang, Jing Shao, Xiang Gao, Lu Sheng
https://arxiv.org/abs/2506.18315

Use Property-Based Testing to Bridge LLM Code Generation and Validation
Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct, especially in complex programming tasks, is a persistent challenge. While traditional Test-Driven Development (TDD) offers a path for code refinement, its efficacy with LLMs is often undermined by the scarcity of high-quality test cases or the pitfalls of automated test generation, including biased tests or inaccurate output predictions that can misdirect the correction process. This pa…

Sampling and Identity-Testing Without Approximate Tensorization of Entropy
William Gay, William He, Nicholas Kocurek, Ryan O'Donnell
https://arxiv.org/abs/2506.23456

Sampling and Identity-Testing Without Approximate Tensorization of Entropy
Certain tasks in high-dimensional statistics become easier when the underlying distribution satisfies a local-to-global property called approximate tensorization of entropy (ATE). For example, the Glauber dynamics Markov chain of an ATE distribution mixes fast and can produce approximate samples in a small amount of time, since such a distribution satisfies a modified log-Sobolev inequality. Moreover, identity-testing for an ATE distribution requires few samples if the tester is given coordinat…

Polynomial Property Testing
Lior Gishboliner, Asaf Shapira
https://arxiv.org/abs/2508.16878 https://arxiv.org/pdf/2508.16878…

Polynomial Property Testing
Property testers are fast, randomized "election polling"-type algorithms that determine if an input (e.g., graph or hypergraph) has a certain property or is $\varepsilon$-far from the property. In the dense graph model of property testing, it is known that many properties can be tested with query complexity that depends only on the error parameter $\varepsilon$ (and not on the size of the input), but the current bounds on the query complexity grow extremely quickly as a function of $1/\varepsil…

from my link log —
Logarithms of algebraic data types for property-based testing.
https://dl.acm.org/doi/abs/10.1145/3498726
saved 2025-04-25

Type-Based Incorrectness Reasoning
Zhe Zhou, Benjamin Delaware, Suresh Jagannathan
https://arxiv.org/abs/2509.01511 https://arxiv.org/pdf/2509.01511…

Type-Based Incorrectness Reasoning
A coverage type generalizes refinement types found in many functional languages with support for must-style underapproximate reasoning. Property-based testing frameworks are one particularly useful domain where such capabilities are useful as they allow us to verify the completeness, as well as safety, of test generators. There is a surprising connection between the kind of underapproximate reasoning coverage types offer and the style of reasoning enabled by recently proposed Incorrectness Logi…

This https://arxiv.org/abs/2407.03883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common…

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Shin Yoo, Paolo Tonella
https://arxiv.org/abs/2507.11199

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
Mutation testing has emerged as a powerful technique for evaluating the effectiveness of test suites for Deep Neural Networks. Among existing approaches, the statistical mutant killing criterion of DeepCrime has leveraged statistical testing to determine whether a mutant significantly differs from the original model. However, it suffers from a critical limitation: it violates the monotonicity property, meaning that expanding a test set may result in previously killed mutants no longer being cla…

Measured Lepton Magnetic Moments
Gerald Gabrielse, Graziano Venanzoni
https://arxiv.org/abs/2507.11268 https://arxiv.org/pdf/2507.112…

Measured Lepton Magnetic Moments
The electron and muon magnetic moment have played, and continue to play, important roles in testing the fundamental mathematical description of physical reality called the Standard Model of particle physics (SM). The electron magnetic moment is the most precisely measured property of an elementary particle and the most precise SM prediction, setting up the most precise confrontation ever between experiment and theory. It enables the most precise test of quantum field theory, and of the fundamen…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein, Todd Millstein, Benjamin Pierce, Guy Van den Broeck

On the Existence of Unbiased Hypothesis Tests: An Algebraic Approach
Andrew McCormack
https://arxiv.org/abs/2506.08259 https://arxiv.…

On the Existence of Unbiased Hypothesis Tests: An Algebraic Approach
In hypothesis testing problems the property of strict unbiasedness describes whether a test is able to discriminate, in the sense of a difference in power, between any distribution in the null hypothesis space and any distribution in the alternative hypothesis space. In this work we examine conditions under which unbiased tests exist for discrete statistical models. It is shown that the existence of an unbiased test can be reduced to an algebraic criterion; an unbiased test exists if and only i…

Permutation patterns in streams
Benjamin Aram Berendsohn
https://arxiv.org/abs/2507.11291 https://arxiv.org/pdf/2507.11291

Permutation patterns in streams
Permutation patterns and pattern avoidance are central, well-studied concepts in combinatorics and computer science. Given two permutations $τ$ and $π$, the pattern matching problem (PPM) asks whether $τ$ contains $π$. This problem arises in various contexts in computer science and statistics and has been studied extensively in exact-, parameterized-, approximate-, property-testing- and other formulations. In this paper, we study pattern matching in a \emph{streaming setting}, when the in…