This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connec...This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu's algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.展开更多
Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people＇s attention. However, since security has a long history, it covers a wide ra...Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people＇s attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Diriehlet Allocation （LDA） tuned using Genetic Algorithm （GA）, to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners.展开更多
In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they oft...In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.展开更多
文摘This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu's algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61572426 and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH17F01.
文摘Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people＇s attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Diriehlet Allocation （LDA） tuned using Genetic Algorithm （GA）, to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 61602403 and 61402406 and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH17F01.
文摘In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.
