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The Oracle Problem in Software Testing: A Survey
IEEE Transactions on Software Engineering, 2015 Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test ...
Phil McMinn, Shin Yoo, Mark Harman
exaly +2 more sources
The Adaptive Lasso and Its Oracle Properties
Journal of the American Statistical Association, 2006 Hui Zou
exaly +2 more sources
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models [PDF]
Neural Information Processing Systems, 2023 Large Language Models (LLMs), despite their recent impressive accomplishments, are notably cost-prohibitive to deploy, particularly for applications involving long-content generation, such as dialogue systems and story writing.
Clark W. Barrett +11 more
semanticscholar +1 more source
DONeRF: Towards Real‐Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks [PDF]
Computer graphics forum (Print), 2021 The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high‐quality scene and lighting information in compact neural networks.
Thomas Neff +7 more
semanticscholar +1 more source
ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers [PDF]
Neural Information Processing Systems, 2023 Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm.
Kexun Zhang +4 more
semanticscholar +1 more source
TOGA: A Neural Method for Test Oracle Generation [PDF]
International Conference on Software Engineering, 2021 Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as bug finding, preventing regressions, and documentation. In terms of documentation, unit tests express a unit'
Elizabeth Dinella +3 more
semanticscholar +1 more source
Oracle Efficient Online Multicalibration and Omniprediction [PDF]
ACM-SIAM Symposium on Discrete Algorithms, 2023 A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous loss minimization guarantees for a large family of loss functions.
Sumegha Garg +3 more
semanticscholar +1 more source
Neural-Based Test Oracle Generation: A Large-Scale Evaluation and Lessons Learned [PDF]
ESEC/SIGSOFT FSE, 2023 Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techniques have shown promise, their real-world effectiveness remains a ...
Soneya Binta Hossain +4 more
semanticscholar +1 more source
Multivariable quantum signal processing (M-QSP): prophecies of the two-headed oracle [PDF]
Quantum, 2022 Recent work shows that quantum signal processing (QSP) and its multi-qubit lifted version, quantum singular value transformation (QSVT), unify and improve the presentation of most quantum algorithms.
Z. M. Rossi, I. Chuang
semanticscholar +1 more source
Online-Extractability in the Quantum Random-Oracle Model [PDF]
IACR Cryptology ePrint Archive, 2021 We show the following generic result. Whenever a quantum query algorithm in the quantum random-oracle model outputs a classical value $t$ that is promised to be in some tight relation with $H(x)$ for some $x$, then $x$ can be efficiently extracted with ...
Jelle Don +3 more
semanticscholar +1 more source