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Software project defect prediction can help developers allocate debugging resources. Existing software defect prediction models are usually based on machine learning methods, especially deep learning. Deep learning‐based methods tend to build end‐to‐end models that directly use source code‐based abstract syntax trees (ASTs) as input. They do not pay enough attention to the front‐end data representation. In this paper, we propose a new framework to represent source code called multiperspective tree embedding (MPT‐embedding), which is an unsupervised representation learning method. MPT‐embedding parses the nodes of ASTs from multiple perspectives and encodes the structural information of a tree into a vector sequence. Experiments on both cross‐project defect prediction (CPDP) and within‐project defect prediction (WPDP) show that, on average, MPT‐embedding provides improvements over the state‐of‐the‐art method.

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Software bug localization is very important in software engineering, but it is also complicated and time consuming. To improve the efficiency of developers, researchers have developed various traditional bug localization and machine learning bug localization methods. In this paper, we propose a novel method that improves bug localization performance. First, surface lexical correlation matching between bug reports and source code files is used to obtain features by deep neural network. Second, to solve the lexical gap between bug reports and source code files, semantic correlation matching between them is used to obtain features based on word embedding and sentence embedding by deep neural network. Then, the joint features obtained by the surface lexical and semantic correlation matching are fused into a unified feature representation for bug reports and source code files. In addition, since our experimental datasets are imbalanced data, we use a focal loss function to solve the impact of data imbalance. Finally, our method obtains the relatively high bug localization performance compared to other classic methods.

ICMANs (Intermittently Connected Mobile Ad hoc Networks) are wireless networks where most of the time there does not exist a complete path from the source to the destination. In this paper, a novel routing approach, SPR (Semi-Probabilistic Routing), is proposed to address routing problem in ICMANs. SPR takes into account information about host mobility and connectivity changes to produce estimates enabling more accurate message forwarding. These include maintaining proactive routing zones for stable local topology to minimize blind message forwarding, and identifying potential carriers to maximize message delivery despite network partitions and intermittent connectivity. We compare the performance of our protocol against others, using a mobility model validated with real-world traces.