Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Novel Accelerating Life Test Method and Its Application by Combining Constan Stress and Progressive Stress

Constant stress accelerated life tests(ALTs) can be applied to obtain a high estimation accuracy of reliability measure?ments, but these are time?consuming tests. Progressive stress ALTs can yield failures more quickly but cannot guaran tee the estimation accuracy of reliability measurements. In this paper, a progressive?constant combination stress ALT is proposed to combine the merits of both tests. The optimal plan, in which the design variables are the initial pro?gressive stress level, the progressive stress ramp rate, the sample allocation proportion of the progressive stress and the constant stress level, is determined using the principle of minimizing the asymptotic variance of the maximum likelihood estimator of the natural log reliable life for the connectors. A comparison between the optimal PCCSALT plan and the CSALT plan with the same sample size and estimation accuracy shows that the test time is reduced by 13.59% by applying the PCCSALT.

Inference and optimal design on step-stress partially accelerated life test for hybrid system with masked data

Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.

Managing Software Testing Technical Debt Using Evolutionary Algorithms

Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.

Test Case Generation from UML-Diagrams Using Genetic Algorithm

Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology.Specialized tools interpret these models into other software artifacts such as code,test data and documentation.The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements.This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the approach.The approach successfully identified defects in various ATM functions such as messaging and operation.

Mixture test strategy optimization for analog systems

Since analog systems play an essential role in modern equipment,test strategy optimization for analog systems has attracted extensive attention in both academia and industry.Although many methods exist for the implementation of effective test strategies,diagnosis for analog systems suffers from the impacts of various stresses due to sophisticated mechanism and variable operational conditions.Consequently,the generated solutions are impractical due to the systems’topology and influence of information redundancy.Additionally,independent tests operating sequentially on the generated strategies may increase the time consumption.To overcome the above weaknesses,we propose a novel approach called heuristic programming(HP)to generate a mixture of test strategies.The experimental results prove that HP and Rollout-HP access the strategy with fewer layers and lower cost consumption than state-of-the-art methods.Both HP and Rollout-HP provide more practical strategies than other methods.Additionally,the cost consumption of the strategy based on HP and Rollout-HP is improved compared with those of other methods because of the updating of the test cost and adaptation of mixture OR nodes.Hence,the proposed HP and Rollout-HP methods have high efficiency.

Model-based optimization of phased array ultrasonic testing

Simulation of phased array beams in dovetail and austenitic welds is conducted to optimize the setup of phased array ultrasonic testing（PAUT）.To simulate the beam in such material with complex geometry or with characteristic of anisotropy and inhomogeneity, firstly,linear phased multi-Gaussian beam（LPMGB） models are introduced and discussed. Then,in the case of dovetail,wedge is designed to maximize the stable amplitude of the beam along the steering path;in the case of austenitic weld,modified focal law are developed to solve the problem of beam screwing and defocusing due to the material properties.To verify the effectiveness of the modified focal law,beam fields are calculated using LPMGB model in austenitic welds.

Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines

The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.

Optimization and experiment on key structural parameters of no-tillage planter with straw-smashing and strip-mulching

Accelerate the quality of smashed-straw laying and enhance the effect of seed-bed arranging for no-tillage planter with straw-smashing and strip-mulching in full stubble covered paddy have become imperative in implementing modern conservation tillage.Considering the perfect operating performance(passability and stability)of the developed no-tillageplanter,this study intends to optimize the structure design of smashed-straw diversion device and strip-rotary tillage device.Dynamics equations of smashed straw and kinematics models of rotary blades were established through theoretical analysis,and the principal factors that affecting straw strip-laying quality and seed-bed arranging effect were specified.The influence of out-enlarge angle(η)and slide-push angle(γ)of the diversion device on the coefficient of variation(ζ1)of cover-straw width,and the influence of rotary tillage-blade number(N)and configuration in a singlerotary plane on the broken rate(ζ2)of strip soil were completely analyzed.And then,based on the systematic analysis and integrated scheme,operating performance and field verification tests using the optimized no-tillage planter were thoroughly performed.The results of the performance tests indicated that the out-enlarge angle(η)had a highly significant influence on the coefficient of variation(ζ1),and the slide-push angle(γ)had a significant influence on(ζ1).The rotary tillage-blade number(N)had a highly significant influence on the broken rate(ζ2),and the slide-push angle(γ)had a significant influence on(ζ2).The obtained optimal combination of these key structure parameters through comprehensive analysis wasη=45°,γ=40°,and N=4.Field verification test results indicated that the optimized no-tillage planter achieved mean values of ζ1=10.47%and ζ2=90.95%,which satisfied the relevant operation quality and cultivation agricultural requirement of conservation tillage equipment,and provided technical references for developing the similar no-tillage planter of straw crushing and returning.

Step-stress accelerated degradation test planning based on Wiener processwith correlation

To assess the lifetime distribution of highly reliable or expensive product,one of the most commonly used strategies is to construct step-stress accelerated degradation test(SSADT)which can curtail the test duration and reduce the test cost.In reality,it is not unusual for a unit with a higher degradation rate which exhibits a more volatile degradation path.Recently,Ye,Chen,and Shen[(2015).A new class of Wiener process models for degradation analysis.Reliability Engineering and System Safety,139,58–67]proposed a Wiener process to capture the positive correlation between the drift rate and the volatility.In this paper,an optimal SSADT plan is developed under the assumption that the underlying degradation path follows the Wiener process with correlation.Firstly,the stochastic diffusion process is introduced to model a typical SSADT problem.Then the design variables,including the sample size,the measurement frequency and the numbers of measurements under each stress level,are optimised by minimising the asymptotic variance of the estimated p-percentile of the product’s lifetime distribution subject to the total experimental cost not exceeding a pre-specified budget.Finally,a numerical example is presented to illustrate the proposed method.

Projection-based High-dimensional Sign Test

This article is concerned with the high-dimensional location testing problem.For highdimensional settings,traditional multivariate-sign-based tests perform poorly or become infeasible since their Type I error rates are far away from nominal levels.Several modifications have been proposed to address this challenging issue and shown to perform well.However,most of modified sign-based tests abandon all the correlation information,and this results in power loss in certain cases.We propose a projection weighted sign test to utilize the correlation information.Under mild conditions,we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives.Benefiting from using the sample-splitting idea for estimating the optimal direction,the proposed test is able to retain type-I error rates pretty well with asymptotic distributions,while it can be also highly competitive in terms of robustness.Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.

OPTIMAL UMP TEST FOR PARAMETER IN THE EXPONENTIAL FAMILY

In this paper the problem of testing the one sided hypothesis in the exponential family against θ>θ is considered. By using stopping time, it is pointed out that under certain condition, the resulting test depending on the incomplete data is proved to be equivalent to the UMP test which depends on the full time spans of the instruments. Finally, the optimal stopping time, which generates the UMP test with level α and has the minimum total experiment time as well. is obtained.

Novel Cluster Validity Index for FCM Algorithm

How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means （FCM）. In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.

STATISTICAL INFERENCE PROCEDURE FOR A BIVARIATE EXPONENTIAL DISTRIBUTION

In this paper, on the basis of the data of both components and series system experiments,we give some statistical inference procedures for the three parameters of a bivariate exponential distribution, and discuss their properties.