Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

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Mathematical Methods Of Statistics

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream’s nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.

On Density-Based Data Streams Clustering Algorithms: A Survey

Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.

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Intrusion Detection System using Support Vector Machine and Decision Tree

The automata-theoretic approach to LTL verification relies on an algorithm for finding accepting cycles in a Biichi automaton. Explicit-state model checkers typically construct the automaton on the fly and explore its states using depth-first search. We survey algorithms proposed for this purpose and identify two good algorithms, a new algorithm based on nested DFS, and another based on strongly connected components. We compare these algorithms both theoretically and experimentally and determine cases where both algorithms can be useful.

A note on on-the-fly verification algorithms

Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.

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Microblog sentiment analysis using social and topic context.

A hierarchical binary tree multi-class support vector machine (BTMSVM) based on class similarity in feature space is improved to overcome the drawbacks such as unclassifiable region which the existent methods have. The class similarity which considers class distance and distribution sphere in feature space is used to determine the classification order of hierarchical multi-class SVM. The learning samples and corresponding SVM sub-classifier are selectively re-constructed to make sure as bigger as classification margin, as much as generalization ability. The results of simulated experiments show that the proposed method is faster in training and classifying, better in classification correctness and generalization.

An Improved Hierarchical Multi-class Support Vector Machine with Binary Tree Architecture

Sentiment analysis is widely studied to extract opinions from user generated content (UGC), and various methods have been proposed in recent literature. However, these methods are likely to introduce sentiment bias, and the classification results tend to be positive or negative, especially for the lexicon-based sentiment classification methods. The existence of sentiment bias leads to poor performance of sentiment analysis. To deal with this problem, we propose a novel sentiment bias processing strategy which can be applied to the lexicon-based sentiment analysis method. Weight and threshold parameters learned from a small training set are introduced into the lexicon-based sentiment scoring formula, and then the formula is used to classify the reviews. In this paper, a completed sentiment classification framework is proposed. SentiWordNet (SWN) is used as the experimental sentiment lexicon, and review data of four products collected from Amazon are used as the experimental datasets. Experimental results show that the bias processing strategy reduces polarity bias rate (PBR) and improves performance of the lexicon-based sentiment analysis method.

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Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias.

Aggregated graph convolutional networks for aspect-based sentiment classification

Back-propagation neural network (BPNN) for software reliability prediction has the problem of network architecture difficult to determine. We want to solve the problem and further increase the prediction accuracy of the model. We use phase space reconstruction technology and the fuzzy wavelet neural network (FWNN), and present the software reliability growth model based on fuzzy wavelet neural network (FWNN-SRGM). The experimental results show that the presented method can easily determine the network architecture according to failure data. The prediction accuracy of FWNN-SRGM is better than that of the widely used BPNN-SRGM.

Jianpei Zhang

Papers

Microblog sentiment analysis using social and topic context.

An Improved Hierarchical Multi-class Support Vector Machine with Binary Tree Architecture

Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias.

Aggregated graph convolutional networks for aspect-based sentiment classification

Software reliability growth model based on fuzzy wavelet neural network