Forecasting and Evaluating the Efficiency of Test Generation Algorithms by Genetic Algorithm

To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.

Five modified boundary scan adaptive test generation algorithms

To study the diagnostic problem of Wire-OR （W-O） interconnect fault of PCB （Printed Circuit Board）, five modified boundary scan adaptive algorithms for interconnect test are put forward. These algorithms apply Global-diagnosis sequence algorithm to replace the equal weight algorithm of primary test, and the test time is shortened without changing the fault diagnostic capability. The descriptions of five modified adaptive test algorithms are presented, and the capability comparison between the modified algorithm and the original algorithm is made to prove the validity of these algorithms.

Efficient Protocol-Proving Algorithm Based on Improved Authentication Tests

A new efficient protocol-proving algorithm was proposed for verifying security protocols. This algorithm is based on the improved authentication tests model, which enhances the original model by formalizing the message reply attack. With exact causal dependency relations between messages in this model, the protocol-proving algorithm can avoid the state explosion caused by asynchronous. In order to get the straight proof of security protocols, three authentication theorems are exploited for evaluating the agreement and distinction properties. When the algorithm terminates, it outputs either the proof results or the potential flaws of the security protocol. The experiment shows that the protocol-proving algorithm can detect the type flaw attack on Neuman-Stubblebine protocol, and prove the correctness of NSL protocol by exploring only 10 states.

Target-Fault-Oriented Test Generation of Sequential CircuitsUsing Genetic Algorithm

This paper deals with the target-fault-oriented test generation of sequential circuits using genetic algorithms. We adopted the concept of multiple phases and proposed four sub-procedures which consist of activation, propagation and justification phases. The paper focuses on the design of genetic operators and construction of fitness functions which are based on the structure information of circuits. Using ISCAS89 benchmarks, the experiment results of GA were given.

Automatic Test Data Generation for Java Card Applications Using Genetic Algorithm

The main objective of software testing is to have the highest likelihood of finding the most faults with a minimum amount of time and effort. Genetic Algorithm (GA) has been successfully used by researchers in software testing to automatically generate test data. In this paper, a GA is applied using branch coverage criterion to generate the least possible set of test data to test JSC applications. Results show that applying GA achieves better performance in terms of average number of test data?generations, execution time, and percentage of branch coverage.

Minimal-Length Interoperability Test Sequences Generation via Genetic Algorithm

A novel interoperability test sequences optimization scheme is proposed in which the genetic algorithm （GA） is used to obtain the minimal-length interoperability test sequences. During our work, the basic interoperability test sequences are generated based on the minimal-complete-coverage criterion, which removes the redundancy from conformance test sequences. Then interoperability sequences minimization problem can be considered as an instance of the set covering problem, and the GA is applied to remove redundancy in interoperability transitions. The results show that compared to conventional algorithm, the proposed algorithm is more practical to avoid the state space explosion problem, for it can reduce the length of the test sequences and maintain the same transition coverage.

Study on MCM Interconnect Test Generation Based on Ant Algorithm with Mutation Operator

A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.

MLSI-RT: memorize LOS range measurements identified residual test location algorithm and performance analysis

The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.

User Session-Based Test Case Generation and Optimization Using Genetic Algorithm

An approach to generating and optimizing test cases is proposed for Web application testing based on user sessions using genetic algorithm. A large volume of meaningful user sessions are obtained after purging their irrelevant information by analyzing user logs on the Web server. Most of the redundant user sessions are also removed by the reduction process. For test reuse and test concurrency, it divides the user sessions obtained into different groups, each of which is called a test suite, and then prioritizes the test suites and the test cases of each test suite. So, the initial test suites and test cases, and their initial executing sequences are achieved. However, the test scheme generated by the elementary prioritization is not much approximate to the best one. Therefore, genetic algorithm is employed to optimize the results of grouping and prioritization. Meanwhile, an approach to generating new test cases is presented using crossover. The new test cases can detect faults caused by the use of possible conflicting data shared by different users.

Recursive Algorithm and Alternate Operation Strategy in Sequential Tests

Based on the sequential probability ratio test(SPRT)developed by Wald,an improved method for successful probability test of missile flight is proposed.A recursive algorithm and its program in Matlab are designed to calculate the real risk level of the sequential test decision and the average number of samples under various test conditions.A concept,that is "rejecting as soon as possible",is put forward and an alternate operation strategy is conducted.The simulation results show that it can reduce the test expenses.

Verification of Damage Detection Algorithm by Prototype Building Structure Test

Safety related accidents of buildings and civil engineering structures have been reported all over the world. With the increasing importance of securing the safety of social infrastructure and optimum performance levels to prevent these accidents, a lot of attention has been concentrated on monitoring performance degradation due to structural defects and deterioration. In this study, the algorithm was developed to evaluate the safety of structures by analyzing signals of time domain and frequency domain, and the developed algorithm was verified through a forced vibration test. From the results of time-domain and frequency-domain data analysis, the damage detection results by each sensor location with a high degree of accuracy were derived in both methods.

Using Genetic Algorithm as Test Data Generator for Stored PL/SQL Program Units

PL/SQL is the most common language for ORACLE database application. It allows the developer to create stored program units (Procedures, Functions, and Packages) to improve software reusability and hide the complexity of the execution of a specific operation behind a name. Also, it acts as an interface between SQL database and DEVELOPER. Therefore, it is important to test these modules that consist of procedures and functions. In this paper, a new genetic algorithm (GA), as search technique, is used in order to find the required test data according to branch criteria to test stored PL/SQL program units. The experimental results show that this was not fully achieved, such that the test target in some branches is not reached and the coverage percentage is 98%. A problem rises when target branch is depending on data retrieved from tables;in this case, GA is not able to generate test cases for this branch.

Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.

Analysis of College Students’ Test Scores Based on Two-Component Mixed Generalized Normal Distribution

In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University. The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students’ test data, and has good application value.

New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation

Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main features of the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input data. In both algorithms, the solution is represented as decomposition on special basic functions, which satisfy given a priori information on solution, and this idea allow us significantly to improve the quality of approximate solution and simplify solving the minimization problem. The theoretical details of the algorithms, as well as the results of numerical experiments for proving robustness of the algorithms, are presented.

Syphilis testing algorithms:A review

The methods and strategies used to screen for syphilis and to confirm initially reactive results can vary significantly across clinical laboratories.While the performance characteristics of these different approaches have been evaluated by multiple studies,there is not,as of yet,a single,universally recommende dalgorithm for syphilis testing.To clarify the currently available options for syphilis testing,this update will summarize the clinical challenges to diagnosis,review the specific performance characteristics of treponemal and non-treponemal tests,and finally,summarize select studies published over the past decade which have evaluated these approaches.Specifically,this review will discuss the traditional and reverse sequence syphilis screening algorithms commonly used in the United States,alongside a discussion of the European Centre for Disease Prevention and Control syphilis algorithm.Ultimately,in the United States,the decision of which algorithm to use is largely dependent on laboratory resources,the local incidence of syphilis and patient demographics.

GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System, GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System, SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS/SINS/视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS/SINS/视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。