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Large-scale time series data are prevalent across diverse application domains including system management, biomedical informatics, social networks, finance, etc. Temporal dependency discovery performs an essential part to identify the hidden interactions among the observed time series and helps to gain more insight into the behavior of the applications. However, the time-varying sparsity of the interactions...
Managing the costs and risks of evolution is a challenging problem in the RE community. The challenge lies in the difficulty of analyzing and assessing the proneness to requirement changes across multiple versions, especially when the scale of requirements is large. In this paper, we define a series of metrics to characterize historic evolution information, and propose a novel method for predicting...
Software maintenance effort constitutes a major portion of the software lifecycle effort. Its estimation is vital for successful project planning and strategic resource allocation. In this paper, we conduct and report an industrial case study in this field. The data set was collected from an industrial software process management tool QONE (formerly SoftPM). The methodology proposed provides corresponding...
More and more software applications are developed within a software ecosystem (SECO), such as the Face book ecosystem and the iPhone AppStore. A core asset of a software ecosystem is its users, and the behavior of the users strongly affects the decisions of software vendors. The number of active users reflects user satisfaction and quality of the applications in a SECO. However, we can hardly find...
The prediction of the dominant migration pathways of oil and gas migration is a key issue. The article starts from the parametric comprehensive analysis of the controlling factors such as transport system, energy field, etc. Through the establishment of the quantitative relationship between the various parameters and the dominant migration pathways, we use spatial analysis methods after the conversion...
Due to the unanticipated influence, the forecast on petroleum price has always been a highly contentious issue. The Binary-Tree pricing model has natural advantages in predicting the fluctuation range of the petroleum price. It can be seen vividly that the future trend of oil price fluctuation and can show the abnormal range of oil price fluctuation range. In this article, the standard model of the...
The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results...
In the smelting process of blast furnace, maintaining the temperature at an acceptable level is the key to ensure the smelting at a good level. The hot metal Silicon content is not only the indication of the blast furnace thermal state and its changes, but also the significant indicator for assessing the blast furnace's stability and the quality of iron. Therefore, as the core content of automatic...
Monitoring and predicting the increasing or decreasing trend of bug number in a software system is of great importance to both software project managers and software end-users. For software managers, accurate prediction of bug number of a software system will assist them in making timely decisions, such as effort investment and resource allocation. For software end-users, knowing possible bug number...
A new model based on improved ant colony algorithm (ACA) and backpropagation (BP) is proposed to predict Silicon content of hot metal in blast furnace. BP algorithm has been widely used in training artificial neural network (ANN), which is an outstanding model to predict Silicon content. BP algorithm has many attractive features, such as adaptive learning, self- organism, and fault tolerant ability...
According to the characteristics of the peak-load, a new approach to power system peak-load forecasting is proposed based on wavelet transform and artificial neural network (ANN). As we all know, the different meteorological type will cause the difference of modes of load, thus it will increase the training time of the neural network and influence the precision of predicting notably. For this reason,...
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