23,584 research outputs found
Predicting software project effort: A grey relational analysis based method
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) from grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential.National Natural Science Foundation
of Chin
Unknown Quantum States: The Quantum de Finetti Representation
We present an elementary proof of the quantum de Finetti representation
theorem, a quantum analogue of de Finetti's classical theorem on exchangeable
probability assignments. This contrasts with the original proof of Hudson and
Moody [Z. Wahrschein. verw. Geb. 33, 343 (1976)], which relies on advanced
mathematics and does not share the same potential for generalization. The
classical de Finetti theorem provides an operational definition of the concept
of an unknown probability in Bayesian probability theory, where probabilities
are taken to be degrees of belief instead of objective states of nature. The
quantum de Finetti theorem, in a closely analogous fashion, deals with
exchangeable density-operator assignments and provides an operational
definition of the concept of an ``unknown quantum state'' in quantum-state
tomography. This result is especially important for information-based
interpretations of quantum mechanics, where quantum states, like probabilities,
are taken to be states of knowledge rather than states of nature. We further
demonstrate that the theorem fails for real Hilbert spaces and discuss the
significance of this point.Comment: 30 pages, 2 figure
Fuzzy C-mean missing data imputation for analogy-based effort estimation
The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model
The LifeV library: engineering mathematics beyond the proof of concept
LifeV is a library for the finite element (FE) solution of partial
differential equations in one, two, and three dimensions. It is written in C++
and designed to run on diverse parallel architectures, including cloud and high
performance computing facilities. In spite of its academic research nature,
meaning a library for the development and testing of new methods, one
distinguishing feature of LifeV is its use on real world problems and it is
intended to provide a tool for many engineering applications. It has been
actually used in computational hemodynamics, including cardiac mechanics and
fluid-structure interaction problems, in porous media, ice sheets dynamics for
both forward and inverse problems. In this paper we give a short overview of
the features of LifeV and its coding paradigms on simple problems. The main
focus is on the parallel environment which is mainly driven by domain
decomposition methods and based on external libraries such as MPI, the Trilinos
project, HDF5 and ParMetis.
Dedicated to the memory of Fausto Saleri.Comment: Review of the LifeV Finite Element librar
On Evidence-based Risk Management in Requirements Engineering
Background: The sensitivity of Requirements Engineering (RE) to the context
makes it difficult to efficiently control problems therein, thus, hampering an
effective risk management devoted to allow for early corrective or even
preventive measures. Problem: There is still little empirical knowledge about
context-specific RE phenomena which would be necessary for an effective
context- sensitive risk management in RE. Goal: We propose and validate an
evidence-based approach to assess risks in RE using cross-company data about
problems, causes and effects. Research Method: We use survey data from 228
companies and build a probabilistic network that supports the forecast of
context-specific RE phenomena. We implement this approach using spreadsheets to
support a light-weight risk assessment. Results: Our results from an initial
validation in 6 companies strengthen our confidence that the approach increases
the awareness for individual risk factors in RE, and the feedback further
allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201
Experimental determination of multipartite entanglement with incomplete information
Multipartite entanglement is very poorly understood despite all the
theoretical and experimental advances of the last decades. Preparation,
manipulation and identification of this resource is crucial for both practical
and fundamental reasons. However, the difficulty in the practical manipulation
and the complexity of the data generated by measurements on these systems
increase rapidly with the number of parties. Therefore, we would like to
experimentally address the problem of how much information about multipartite
entanglement we can access with incomplete measurements. In particular, it was
shown that some types of pure multipartite entangled states can be witnessed
without measuring the correlations [M. Walter et al., Science 340, 1205 (2013)]
between parties, which is strongly demanding experimentally. We explore this
method using an optical setup that permits the preparation and the complete
tomographic reconstruction of many inequivalent classes of three- and
four-partite entangled states, and compare complete versus incomplete
information. We show that the method is useful in practice, even for non-pure
states or non ideal measurement conditions.Comment: 12 pages, 7 figures. Close to published versio
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