Yikun Zhang

An improved quantum genetic algorithm for test suite reduction

Test suite reduction is necessary to reduce the testing cost and increase efficiency, therefore, test suite reduction model is proposed, and test suite reduction problem is converted into a standard optimization problem. Additionally, an improved e volutionary algorithm is also proposed, which encodes the chromosome with quantum bit as its basic information bit. It achieves the individual evolution by improving the quantum rotating gates using adaptive rotation, and its convergence speed and global optimization ability is superior to the traditional evolutionary algorithm. Motivated by this, we propose a novel test suite reduction method using quantum evolutionary. Finally, experiments validate the technology with higher efficiency.

An EMD-based denoising method for lidar signal

Lidar echo signal is a typical non-steady-state, non-stationary signal, and difficult to be dealt with by the traditional filtering methods. As a new signal processing theory proposed in recent years, Empirical Mode Decomposition method can adaptively divide the lidar echo signal into different intrinsic mode function (IMF) components according to different time scale, and noise mainly concentrates in the high-frequency component. However, when filtered with simply removing high frequency component, the useful signal will be possible to be reduced. In this paper, a new method which combines Empirical Mode Decomposition (EMD) with Savitzky-Golay filter is proposed. With experiments, it is indicated that our approach not only removes the noise component effectively but also maintains the useful signal, then will improve the accuracy in the next phase of data processing.

A qualitative and quantitative risk assessment method in software security

Focusing on the software security risk assessment, this paper adopts the combination of the attack tree model analysis and the Bayesian Network analysis, which takes the advantage of both qualitative analysis and quantitative analysis to assess risks of software security. By the construction and pruning of the attack tree model, this method narrow down the scope of threats that are generated by software system at first. With a preliminary control of risk probability, and through the prior probability value, the conditional probability table and the Bayesian formula, this method can assess the risk probability of the software system accurately. Finally the risk assessment method makes up the deficiency of single risk assessment method, won more accurate evaluation results. It can obtain the software project risk rank more accurate and carries on the defense and the recovery to the risk partial modules.

An Lidar data compression method based on improved LZW and Huffman algorithm

Lidar raw echo data has characteristics such as huge data quantity, strong discreteness and unpredictability. According to the construction of Lidar monitoring network of atmospheric environment, the existing network can not provide enough bandwidth to transmit Lidar data in real time. In this paper, we propose a novel hybrid lossless compression algorithm to reduce the transmission amount, namely the probability statistics lossless compression algorithm base on the improved LZW(Lempel-Ziv-Welch), which combines Huffman coding. With experiment on the raw two-value atmospheric data, we verify the effectiveness of our approach that the compression ratio is close to 9.5:1 and the coding efficiency is up to 98%.

An edge crossing minimization algorithm based on adjacency matrix transformation

In software testing and maintenance, how to obtain useful information and relationships from large amounts of information quickly becomes an urgent problem which is required to be solved. Visualization technology is one of the effective tools. The tree structure of hierarchical graph can effectively display hierarchical structure of software testing and maintenance information, and which can help software maintainers to understand the software. Minimizing edge crossing is directly involved with the readability of the graph in drawing hierarchical graph. An optimization is given for minimizing edge crossing problem based on characteristics of adjacency matrix. The representation and transformation methods of hierarchical graphs adjacency matrix are given. Using the algorithm we can achieve the aims of optimization for edge crossing minimization during experimental results.

Atmospheric Lidar Data Storage Model Based on Ontology

Ontology is an effective method to solve the problem of heterogeneous data in lidar measurements. Due to complexity and diversity of data structure, traditional method of ontology storage cannot be directly applied to lidar data. In this work, we proposed a novel ontology storage model based on the object-oriented data model, in which the mapping mechanism was established from ontology of lidar data to the object-oriented data model. A new storage model of lidar data is then obtained by a combination of the characteristics of lidar data and the syntax of OWL DL. Compared to the traditional method of ontology storage, we believe that the new storage model can better serve the sharing of lidar data.

A Rendering Algorithm for Function Call Diagram in Software Reliability Assurance

The function call diagram is the important tool of ensuring software reliability and safety. Building a function call diagram is very crucial in understanding the program and ensuring software reliability. After summarizing the current popular build tools for function call diagram generation, based on the Clang structure analyzer, according to the characteristics of high level language, using SVG graphics display technology, we design a new method for function call diagram display. Experiments show that the method can generate the function call diagram accurately, as well as improve the drawing efficiency and quality of diagrams, in order to provide graphics support for the reliability of software.

Study of Test Suite Reduction Based on Quantum Evolutionary Algorithm

Test suite reduction aims at adequately testing all the test requirements with the least number of test cases. Through the coverage relationship between test requirements and test cases, test suite reduction problem can be converted into the standard optimization problem. Quantum evolutionary algorithm (QEA) is an intelligent algorithm based on quantum computation . In QEA, chromosomes are encoded with quantum bits as the basic information bits, and individual variation evolution is realized by quantum mutation based on quantum probability gates. So in QEA, the convergence speed and ability to search global best results are superior to the traditional evolutionary algorithms. Motivated by this, we propose a novel test suite reduction method using quantum evolutionary. Finally, experiments validate the technology with higher efficiency.