The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Porting a testing environment to a cloud infrastructure is not straightforward. This paper presents O!Snap, an approach to generate test plans to cost-efficiently execute tests in the cloud. O!Snap automatically maximizes reuse of existing virtual machines, and interleaves the creation of updated test images with the execution of tests to minimize overall test execution time and/or cost. In an evaluation...
Symbolic execution is a well-studied method that has a number of useful applications, including generation of high-quality test suites that find many bugs. However, scaling it to real-world applications is a significant challenge, as it depends on the often expensive process of solving constraints on program inputs. Our insight is that when the goal of symbolic execution is test generation, non-semantics-preserving...
We present a formal optimization technique that enables retargeting for codeword-based IEEE 1149.1-compliant TAP controllers. The proposed method addresses the problem of high test data volume and Test Application Time (TAT) for a system-on-chip design during board or in-field testing, as well as during debugging. This procedure determines an optimal set of codewords with respect to given hardware...
The task of person re-identification (re-id) is to match images of people observed in different camera views. Recent researches mainly focus on feature representation and metric learning. Many global metric learning approaches have achieved good performance. Since comparing all of the samples with a single global metric is inappropriate to handle heterogeneous data, some local metric learning approaches...
A reconfigurable over-the-air chamber represents a reverberation chamber whose walls are lined with antennas that are terminated in reconfigurable impedances, allowing synthesis of a wide range of channel conditions for over-the-air testing of mobile wireless devices. While these chambers have potential for practical device testing, finding the right impedances to achieve the desired channel characteristics...
Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test...
Exhaustive testing of highly configurable software developed in continuous integration is rarely feasible in practice due to the configuration space of exponential size on the one hand, and strict time constraints on the other. This entails using selective testing techniques to determine the most failure-inducing test cases, conforming to highly-constrained time budget. These challenges have been...
The purpose of this paper is to present the use of uniform design of experiments method in improving the permanent deformation and mass of an on-road bicycle frame which undergoes the drop-mass and drop-frame impact tests. Six system parameters of the bicycle frame are selected as the control factors to be improved. Uniform design of experiment is applied to create a set of simulation experiments...
We present strategies for metamorphic testing of compilers using opaque value injection, and experiences using the method to test compilers for the OpenGL shading language.
In this paper we consider the problem of spectrum sensing in cognitive radio networks which involves detection of primary (licensed) users (PUs) by secondary (unlicensed) users (SUs), who are interested in transmitting their data opportunistically. To facilitate accurate detection of PUs by the fusion center (FC) based on the energy measurements received from the chosen set of SUs, we formulate an...
Testing configurable software for high assurancesystems developed in continuous integration requires effectivetechniques for selecting failure-inducing test cases, thoroughlycovering entire configuration space, while providing rapid feedbackon failures. This involves satisfying multiple objectives:maximizing test fault detection, maximizing test coverage ofthe configuration space, and minimizing test...
The paper studies the efficiency of a QPSO (Quantum-behaved Particle Swarm Optimization) algorithm enhanced with neighborhood strategies to solve a NDET (Non-Destructive Electromagnetic Testing) inverse problem, formulated as an optimization problem. Two different neighborhood strategies are analyzed and compared with the classic QPSO, one based on disjoint subswarms, and the other based on informants...
The traditional approach of teaching programming courses is teachers centric where students are passive learners. Also for such courses, the laboratory and classes are conducted separately. This paper focuses on integrating classroom and laboratory with hands-on for programming course. This approach is student centric which brings in active learning. However it has been less researched area and adequate...
General Purpose Graphic Processing Unit(GPGPU) is used widely for achieving high performance or high throughput in parallel programming. This capability of GPGPUs is very famous in the new era and mostly used for scientific computing which requires more processing power than normal personal computers. Therefore, most of the programmers, researchers and industry use this new concept for their work...
Regression testing is an important activity performed to ensure thatchanges in the baseline version of the system do not influence thealready tested part of the system. It becomes difficult to run the entiretest suite due to constrained or limited resources. A subset of test casesthat is as efficient as the original test suite is searched as optimal suite.Computational intelligence approaches has...
The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes [1], [2]. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically...
Classifier competence is critical important for dynamic classifier selection. This study proposes a semi-supervised learning algorithm to learn the competence of classifiers under the proposed optimization framework based on graph. First it constructs a graph based on the training data and some unlabeled data. Then it iteratively learns the competence of classifiers. The learned competence not just...
Multi-class learning from network data is an important but challenging problem with many applications, including malware detection in computer networks, user modeling in social networks, and protein function prediction in biological networks. Despite the extensive research on large multi-class learning, there are still numerous issues that have not been sufficiently addressed, such as efficiency of...
Based on minimum reconstruction error criterion and the intrinsic sparse property of natural data, sparse representation (SR) has shown promising performance on various image recognition tasks. However, in the field of person re-identification (re-id), the state-of-the-art is still dominated by other methods such as metric learning or CNN. It is because samples in one view may not be representative...
In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. Traditional spectrum sensing assumes Gaussian noise (or of other given distributions) in general. However when secondary users (SUs) have no prior information about the measurement distributions, the spectrum sensing schemes assuming given distribution...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.