986 research outputs found
Prioritizing MCDC test cases by spectral analysis of Boolean functions
Test case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost-effective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The
method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing
On the Analysis of Boolean Functions and Fourier-Entropy-Influence Conjecture
This manuscript includes some classical results we select apart from the new
results we've found on the Analysis of Boolean Functions and
Fourier-Entropy-Influence conjecture. We try to ensure the self-completeness of
this work so that readers could probably read it independently. Among the new
results, what is the most remarkable is that we prove that the entropy of a
boolean function could be upper bounded by . This is possibly the only untrivial bound for the entropy up to
now
Harmonic analysis of neural networks
Neural networks models have attracted a lot of
interest in recent years mainly because there
were perceived as a new idea for computing.
These models can be described as a network in
which every node computes a linear threshold
function. One of the main difficulties in analyzing
the properties of these networks is the fact
that they consist of nonlinear elements. I will
present a novel approach, based on harmonic
analysis of Boolean functions, to analyze neural
networks. In particular I will show how this
technique can be applied to answer the following
two fundamental questions (i) what is the computational
power of a polynomial threshold element
with respect to linear threshold elements?
(ii) Is it possible to get exponentially many spurious
memories when we use the outer-product
method for programming the Hopfield model
MCMC Learning
The theory of learning under the uniform distribution is rich and deep, with
connections to cryptography, computational complexity, and the analysis of
boolean functions to name a few areas. This theory however is very limited due
to the fact that the uniform distribution and the corresponding Fourier basis
are rarely encountered as a statistical model.
A family of distributions that vastly generalizes the uniform distribution on
the Boolean cube is that of distributions represented by Markov Random Fields
(MRF). Markov Random Fields are one of the main tools for modeling high
dimensional data in many areas of statistics and machine learning.
In this paper we initiate the investigation of extending central ideas,
methods and algorithms from the theory of learning under the uniform
distribution to the setup of learning concepts given examples from MRF
distributions. In particular, our results establish a novel connection between
properties of MCMC sampling of MRFs and learning under the MRF distribution.Comment: 28 pages, 1 figur
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