this is the continuation of research addressing the performance of a subset of a specific class of software reliability models - the nonhomogeneous Poisson Process (NHPP) models. the object of this paper is to determi...
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ISBN:
(纸本)9781934272367
this is the continuation of research addressing the performance of a subset of a specific class of software reliability models - the nonhomogeneous Poisson Process (NHPP) models. the object of this paper is to determine the reliability of software during the early stages of the system engineering period using software reliability growthmodels (SRGMs). Actual software failure data are analyzed withthe early stage of system test being represented as the 30% percentile of the total test time of each dataset. the study takes three well known NHPP models and determines their ability to estimate reliability during the defined early stages of the system testing period. the predictive quality of each of the models on test is examined. the method of parameter estimation for the models is the Maximum Likelihood Method. Results of the study also address the reliability growth of the failure data for the datasets on test.
software architectures can be represented in many different ways, e.g., using formalized notations, such as SDL, or less formal visualizations, like UML. Besides the notation, the representation also includes mechanis...
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ISBN:
(纸本)9780889867765
software architectures can be represented in many different ways, e.g., using formalized notations, such as SDL, or less formal visualizations, like UML. Besides the notation, the representation also includes mechanisms for reasoning about the various concerns addressed in the architecture, e.g., functional aspects or run-time issues. However, deciding on an appropriate architecture representation in a software development project is a nontrivial task. It mainly depends on the primary purpose of the architecture - or the individual models of the architecture in a particular context, the development resources available, and the expertise of developers involved. In this paper we present a framework that helps define and select an architecture representation for a particular purpose. Once a representation has been defined or selected, it can be used to create the actual architecture for a concrete software system. thus, a representation determines the output format of a software architecture process. In the paper we also use this framework to define a view-based representation as an example of deploying the framework.
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and ...
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ISBN:
(纸本)9781605580791
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naive Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.
Real life work processes are dynamic and much richer in variation to be expressed by typical static workflow models. Two conflicting goals to be addressed include: flexibility to handle changing situations, and simpli...
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ISBN:
(纸本)9780889867765
Real life work processes are dynamic and much richer in variation to be expressed by typical static workflow models. Two conflicting goals to be addressed include: flexibility to handle changing situations, and simplicity to design workflows that can be understood and implemented efficiently. this paper addresses these two issues by introducing a novel architecture for workflow systems. In this architecture, a workflow is defined in terms of modules and templates. Modularity provides simplicity and reusability for a workflow system. In our approach, problem independent modules are adopted for a special workflow system by placing them in problem dependent templates. Also different users in the system can interact withthe workflow to make appropriate changes via events. Modules and templates have their own event handlers that allow them to be modified based on changes in user requests or situations. We illustrate these features in our architecture using two case studies, one in healthcare domain and another in a banking system.
this paper presents a new image preprocessing and revised feature extraction methods for sign language recognition (SLR) based on Hidden Markov models (HMMs). Multi-layer Neural Network is used for building an approxi...
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ISBN:
(纸本)9780769532639
this paper presents a new image preprocessing and revised feature extraction methods for sign language recognition (SLR) based on Hidden Markov models (HMMs). Multi-layer Neural Network is used for building an approximate skin model by using Cb and Cr color components of sample pixels. Gesture videos are spitted into image sequences and converted into YCbCr color space. In order to get only hand area in each image, unexpected skin areas such as face of actor and noises are identified and eliminated. After obtaining hand areas from image sequence of each gesture, features such as direction, center of gravity, length, and so on will be taken out for learning and testing phases. the features will be normalized before used as inputs of HMMs for learning models and recognizing gesture activities.
the advances in communication frameworks, such as Skype and Google Talk facilitate the increasing needs of communication-intensive and collaborative applications. these communication frameworks also make it possible f...
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ISBN:
(纸本)9780889867765
the advances in communication frameworks, such as Skype and Google Talk facilitate the increasing needs of communication-intensive and collaborative applications. these communication frameworks also make it possible for end-users to be more involved in the development of such applications if the appropriate level of abstraction can be provided. In this paper, we propose the design of a user-centric communication middleware (UCM) that supports raising the level of abstraction appropriate for end-users to create and realize models using the communication virtual machine (CVM) technology. the CVM technology consists of the communication modeling language (CML) and CVM, and supports the rapid conception, construction and realization of new communication services using a model-driven approach. the UCM is a layer in CVM that provides operating simplicity to the end-user by masking the underlying technology. We present the design goals of UCM, high-level architecture, a description of the runtime environment and a case study showing how the communication needs of a medical scenario is realized in the UCM.
software effort estimation is still a significant challenge for software management. Although Functional Size Measurement (FSM) methods have been standardized and have become widely used by the software organizations,...
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ISBN:
(纸本)9783540695646
software effort estimation is still a significant challenge for software management. Although Functional Size Measurement (FSM) methods have been standardized and have become widely used by the software organizations, the relationship between functional size and development effort still needs further investigation. Most of the studies focus on the project cost drivers and consider total software functional size as the primary input to estimation models. In this study, we investigate whether using the functional sizes of different functionality types, represented by the Base Functional Component (BFC) types;instead of using the total single size figure have a significant impact on estimation reliability. For the empirical study, we used the projects data in the internationalsoftware Benchmarking Standards Group (ISBSG) Release 10 dataset, which were sized by the COSMIC FSM method.
this paper presents a single project experiment on the fault revealing capabilities of model-based test sets. the tests are generated from UML statecharts and UML sequence diagrams. this experiment found that the stat...
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ISBN:
(纸本)9780769532639
this paper presents a single project experiment on the fault revealing capabilities of model-based test sets. the tests are generated from UML statecharts and UML sequence diagrams. this experiment found that the statechart test sets did better at revealing unit level faults than the sequence diagram test sets, and the sequence diagram test sets did better at revealing integration level faults than the statechart test sets. the statecharts also resulted in more test cases than the sequence diagrams. the results show that model-based testing can be used to systematically generate test data and indicates that different UML models can play different roles in testing.
One of the challenges in designing computer networks is "queue management and congestion avoidance". there are several studies for congestion reduction and controlling such as Random Early Detection (RED) an...
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ISBN:
(纸本)9780769532639
One of the challenges in designing computer networks is "queue management and congestion avoidance". there are several studies for congestion reduction and controlling such as Random Early Detection (RED) and its variants. More recent works on developing congestion avoidance methods include modeling a TCP flow in an Active Queue Management (AQM) of a bottlenecked network link. Rather than classical control theories, that are applied to improve performance and stability of network flows, some studies are developed based on new control tools such as neural networks. In this article a neuro-fuzzy controller for Active Queue Management is proposed. In this model, the number of neurons are determined according to complexity of the model and instead of using random values for initializing network's weights, near to optional values are used. the proposed method decreases the network training time and ensures convergence with higher probability. the results of this method show superior performance over other previous controllers such as classical and neural networks methods.
the PROMISE workshop seeks to deliver to the softwareengineering community useful, usable, verifiable, and repeatable models. To provide a sound and realistic basis for creating predictivemodels, and to allow resear...
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ISBN:
(纸本)9781605580791
the PROMISE workshop seeks to deliver to the softwareengineering community useful, usable, verifiable, and repeatable models. To provide a sound and realistic basis for creating predictivemodels, and to allow researchers to conduct repeatable softwareengineering experiments, we maintain the PROMISE repository, a growing collection that now contains 57 empirically-based data sets.
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