Abstract Bug localization (BL) is known as one of the major steps in the program repair process, which generally seeks to find a set of commands causing a program to crash or fail. At the present time, locating bugs and their sources quickly seems to be impossible as the complexity of modern software development and scaling is soaring. Accordingly, there is a huge demand for BL techniques with minimal human intervention. A graph representing source code typically encodes valuable information about both the syntactic and semantic structures of programs. Many software bugs are associated with these structures, making graphs particularly suitable for bug localization (BL). Therefore, the key contributions of this work involve labeling graph nodes, classifying these nodes, and addressing imbalanced classifications within the graph data structure to effectively locate bugs in code. A graph-based bug classifier is initially introduced in the method proposed in this paper. For this purpose, the program source codes are mapped to a graph representation. Since the graph nodes do not have labels, the Gumtree algorithm is then exploited to label them by comparing the buggy graphs and the corresponding bug-free ones. Afterward, a trained, supervised node classifier, developed based on a graph neural network (GNN), is applied to classify the nodes into buggy or bug-free ones. Given the imbalance in the data, accuracy, precision, recall, and F1-score metrics are used for evaluation. Experimental results on identical datasets show that the proposed method outperforms other related approaches. The proposed approach effectively localizes a broader spectrum of bug types, such as undefined properties, functional bugs, variable naming errors, and variable misuse issues .

Abstract One of the main problems in viral marketing is influence maximization (IM). With a social network and a predefined propagation model, the aim is to seek a subset of nodes that spread the influence widely into the network. Most scalable methods with provable approximation guarantees are presented for this problem based on the reverse influence sampling (RIS) framework. The RIS framework has two phases: sampling and node selection. The sampling phase encountered two challenges in the sampling phase: the number of required samples and the sampling method. Most methods have focused on the first challenge, that is, sample size, and have tried to provide a rigid sample size. In this paper, we focus on the second challenge: how to improve the precision of sampling. We propose to use stratified sampling rather than simple random sampling. Since the degree of each node is one of the affecting factors in the diffusion process. This issue leads us to use stratified sampling based on a degree distribution. The results show that with the application of the proposed method, the solution can estimate with fewer samples, which is faster than the state‐of‐the‐art methods.

In this paper, a new wavelet based image fusion method is proposed. In this method, the blur levels of the edge points are estimated for every slice in the stack of images. Then from corresponding edge points in different slices, the sharpest one is brought to the final image and others are eliminated. The intensities of non-edge pixels are assigned by the slice of its nearest neighbor edge. Results are promising and outperform other methods in most cases of the tested methods.