In this paper we present an algorithm to perform precoder adaptation and power control for rate maximization problem in ad-hoc networks where individual transmitterreceiver pairs communicate in interference channel sc...
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In large software organizations with a product line development approach, system test planning and scope selection is a complex tasks for which tool support is needed. Due to repeated testing: across different testing...
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In large software organizations with a product line development approach, system test planning and scope selection is a complex tasks for which tool support is needed. Due to repeated testing: across different testing levels, over time (test for regression) as well as of different variants, the risk of double testing is large as well as the risk of overlooking important tests, hidden by the huge amount of possible tests. This paper discusses the need and challenges of providing decision support for test planning and test selection in a product line context, and highlights possible paths towards a pragmatic implementation of context-specific decision support of various levels of automation. With existing regression testing approaches it is possible to provide automated decision support in a few specific cases, while test management in general may be supported through visualization of test execution coverage, the testing space and the delta between the sufficiently tested system and the system under test. A better understanding of the real world context and how to map research results to the same is needed.
This paper presents a new approach of extracting local relative texture feature from ultrasound medical images using the Gray Level Run Length Matrix (GLRLM) based global feature. To adapt the traditional global appro...
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ISBN:
(纸本)9781424497881;9781424497898
This paper presents a new approach of extracting local relative texture feature from ultrasound medical images using the Gray Level Run Length Matrix (GLRLM) based global feature. To adapt the traditional global approach of GLRLM -based feature extraction method, a three level partitioning of images has been proposed that enables capturing of local features in terms of global image properties. Local relative features are then calculated as the absolute difference of the global features of each lower layer partition sub-block and that of its corresponding upper layer partition block. Performance of the proposed local relative feature extraction method has been verified by applying it in classifying ultrasound medical images of ovarian abnormalities. Besides, significant improvement has been noticed by comparing the proposed method with traditional GLRLM -based feature extraction method in terms of image classification performance.
Background: History based regression testing was proposed as a basis for automating regression test selection, for the purpose of improving transparency and test efficiency, at the function test level in a large scale...
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Background: History based regression testing was proposed as a basis for automating regression test selection, for the purpose of improving transparency and test efficiency, at the function test level in a large scale software development organization. Aim: The study aims at investigating the current manual regression testing process as well as adopting, implementing and evaluating the effect of the proposed method. Method: A case study was launched including: identification of important factors for prioritization and selection of test cases, implementation of the method, and a quantitative and qualitative evaluation. Results: 10 different factors, of which two are history-based, are identified as important for selection. Most of the information needed is available in the test management and error reporting systems while some is embedded in the process. Transparency is increased through a semi-automated method. Our quantitative evaluation indicates a possibility to improve efficiency, while the qualitative evaluation supports the general principles of history-based testing but suggests changes in implementation details.
During software testing, defect prediction approaches measure current reliability status, forecasting future program failures, and provide information on how many defects need to be removed before shipping. Existing a...
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ISBN:
(纸本)9783642218262
During software testing, defect prediction approaches measure current reliability status, forecasting future program failures, and provide information on how many defects need to be removed before shipping. Existing approaches often require faults to be detected and identified as a new one, before a model-based trend can be fitted. While during regression testing failures may frequently occur, it is not evident which are related to new faults. Consequently, reliability growth trending can only be performed in sync with fault identification and repair, which is often performed in between regression test cycles. In this paper we present a dynamic, reasoning approach to estimate the number of defects in the system early in the process of regression testing. Our approach, coined Dracon, is based on Bayesian fault diagnosis over abstractions of program traces (also known as program spectra). Experimental results show that Dracon systematically estimates the exact number of (injected) defects, provided sufficient tests cases are available. Furthermore, we also propose a simple, analytic performance model to assess the influence of failed test cases in the estimation. We observe that our empirical findings are in agreement with the model.
The realizability is a well known property for reactive system specification, and it shows that there exists a system which reacts properly against any environment following the specification. In system development pr...
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The realizability is a well known property for reactive system specification, and it shows that there exists a system which reacts properly against any environment following the specification. In system development process, we can reduce reversions if realizability for specification is verified before implementation of system. However, the verification cost is too high to verify the realizability of a real scale system specification. On the other hand, stepwise satisfiability which is a necessary condition for the realizability has been proposed. Its verification cost is less than that of realizability, and verification of the property is useful to find the incompleteness of specification. Even if the verification of stepwise satisfiability, the required cost is too high. In order to overcome this problem, this paper proposes methods of implementing the stepwise satisfiability checker using a distributed object technology on the parallel distributed environment. Then, we implement the stepwise satisfiability checker in Java based on our methods, and confirm the effectiveness of our methods by experiments. In this paper, we present our ideas and methods of the implementations for three parts of our checker. The first is how our checker compose a tableau graph on the parallel distributed environment. The second is the implementation method to determinize the distributed graph. The third is the dead end checking method for the deterministic distributed graph.
The objective of this workshop is to bring together researchers and practitioners to report and discuss the challenges and opportunities for integrating quantitative methods in product line engineering with the object...
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A good estimation tool offers a "model" of a project and is usually used to estimate cost and schedule, but it can also be used to help make trade decisions that affect cost and schedule as well as to estima...
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Brain computer Interface (BCI) enables the capturing and processing of motor imagery related brain signals which can be interpreted by computers. BCI systems capture the motor imagery signals via Electroencephalogram ...
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Brain computer Interface (BCI) enables the capturing and processing of motor imagery related brain signals which can be interpreted by computers. BCI systems capture the motor imagery signals via Electroencephalogram or Electrocorticogram. The processing of the signal is usually attempted by extracting feature vectors in the frequency domain and using classification algorithms to interpret the motor imagery action. In this paper we investigate the motor imagery signals obtained from BCI competition dataset IVA using the Fast Hartley Transform (FHT) for feature vector extraction and feature reduction using support vector machine. The processed data is trained and classified using the Bayes Net.
The current industry trend is towards using Commercially available Off-The-Shelf (COTS) based multicores for developing real time embedded systems, as opposed to the usage of custom-made hardware. In typical implement...
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The current industry trend is towards using Commercially available Off-The-Shelf (COTS) based multicores for developing real time embedded systems, as opposed to the usage of custom-made hardware. In typical implementation of such COTS-based multicores, multiple cores access the main memory via a shared bus. This often leads to contention on this shared channel, which results in an increase of the response time of the tasks. Analyzing this increased response time, considering the contention on the shared bus, is challenging on COTS-based systems mainly because bus arbitration protocols are often undocumented and the exact instants at which the shared bus is accessed by tasks are not explicitly controlled by the operating system scheduler; they are instead a result of cache misses. This paper makes three contributions towards analyzing tasks scheduled on COTS-based multicores. Firstly, we describe a method to model the memory access patterns of a task. Secondly, we apply this model to analyze the worst case response time for a set of tasks. Although the required parameters to obtain the request profile can be obtained by static analysis, we provide an alternative method to experimentally obtain them by using performance monitoring counters (PMCs). We also compare our work against an existing approach and show that our approach outperforms it by providing tighter upper-bound on the number of bus requests generated by a task.
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