Model driven development (MDD) is considered a promising approach for software development. In this paper the results of a systematic survey is reported to identify the state-of-the-art within the topic of security in...
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Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on ...
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Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.
This paper proposes a data hiding method by secret sharing. The proposed method embeds a k-bit secret digit in a secret message (i.e., a bit stream, a secret image) into a cover pixel of a grayscale cover image at a t...
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Functional hardware verification is one of the most challenging areas in the hardware design cycle. With the increase in the complexity and size of the design, the time needed for verification becomes the largest part...
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Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extens...
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Service orchestrations as architectural specifications specify collaborating services and the way they interoperate via information exchange. Some orchestration specifications, which describe the behavior of service-o...
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Data skew, cluster heterogeneity, and network traffic are three issues that significantly influence the performance of MapReduce applications. However, the Hash-Partitioner in native Hadoop does not consider them. Thi...
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ISBN:
(纸本)189170642X
Data skew, cluster heterogeneity, and network traffic are three issues that significantly influence the performance of MapReduce applications. However, the Hash-Partitioner in native Hadoop does not consider them. This paper proposes a new partitioner in Yarn (Hadoop 2.6.0), namely, PIY, which adopts an innovative parallel sampling method to achieve the distribution of the intermediate data. Based on this, firstly, PIY mitigates data skew in MapReduce applications. Secondly, PIY considers the heterogeneity of the computing resource to balance the load among Reducers. Thirdly, PIY reduces the network traffic in shuffle phase by trying to retain intermediate data on those nodes who act as both mapper and reducer. Compared with the native Hadoop and some other popular strategies, PIY can reduce the execution time by 35.62% and 50.65% in homogeneous and heterogeneous cluster, respectively. We also implement PIY in parallel image processing. Compared with several existing strategies, PIY can reduce the execution time by 11.2%.
In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNet...
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Conversational systems have become an element of everyday life for billions of users who use speech-based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart...
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Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** le...
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Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.
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