Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.Genetic algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician tofind the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the t-Test.The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.

Impact of elliptical boreholes on in situ stress estimation from leak-off test data

We developed an inversion technique to determine in situ stresses for elliptical boreholes of arbitrary trajectory. In this approach, borehole geometry, drilling-induced fracture information, and other available leak-off test data were used to construct a mathematical model, which was in turn applied to finding the inverse of an overdetermined system of equations.The method has been demonstrated by a case study in the Appalachian Basin, USA. The calculated horizontal stresses are in reasonable agreement with the reported regional stress study of the area, although there are no field measurement data of the studied well for direct calibration. The results also indicate that 2% of axis difference in the elliptical borehole geometry can cause a 5% difference in minimum horizontal stress calculation and a 10% difference in maximum horizontal stress calculation.

MC/DC Test Data Generation Algorithm Based on Whale Genetic Algorithm

The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements.This paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC coverage.The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation.At the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic al-gorithm.The threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the population.Finally,an improved crossover strategy is pro-posed to accelerate the convergence of the algorithm.The improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark programs.The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.

DAMAGE DETECTION IN BUILDINGS USING A TWO-STAGE SENSITIVITY-BASED METHOD FROM MODAL TEST DATA

Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.

Processing the rig test data of an air filling twin-tube shock absorber

A separation method is proposed to design and improve shock absorber according to the characteristics of each force. The method is validated by rig test. The force data measured during rig test is the resultant force of damping force, rebound force produced by pressed air, and friction force. Different characters of damping force, air rebound force and friction force can be applied to seperate each force from others. A massive produced air filling shock absorber is adopted for the validation. The statistic test is used to get the displacement-force curves. The data are used as the input of separation calculation. Then the tests are carried out again to obtain the force data without air rebound force. The force without air rebound is compared to the data derived from the former tests with the separation method. The result shows that this method can separate the damping force and the air elastic force.

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development.Current fastpaced system developments have rendered traditional testing techniques obsolete.Therefore,automated testing techniques are needed to adapt to such system developments speed.Model-based testing(MBT)is a technique that uses system models to generate and execute test cases automatically.It was identified that the test data generation(TDG)in many existing model-based test case generation(MB-TCG)approaches were still manual.An automatic and effective TDG can further reduce testing cost while detecting more faults.This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model(EFSM).The proposed approach integrates MBT with combinatorial testing.The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach.The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventionalMB-TCG but at the same time generated 43 more tests.The proposed approach effectively detects faults,but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

Calibration of Four Nonlinear Failure Envelopes from Triaxial Test Data and Influence of Nonlinearity on Geotechnical Computations

It is now recognized that many geomaterials have nonlinear failure envelopes. This non-linearity is most marked at lower stress levels, the failure envelope being of quasi-parabolic shape. It is not easy to calibrate these nonlinear failure envelopes from triaxial test data. Currently only the power-type failure envelope has been studied with an established formal procedure for its determination from triaxial test data. In this paper, a simplified procedure is evolved for the development of four different types of nonlinear envelopes. These are of invaluable assistance in the evaluation of true factors of safety in problems of slope stability and correct computation of lateral earth pressure and bearing capacity. The use of the Mohr-Coulomb failure envelopes leads to an overestimation of the factors of safety and other geotechnical quantities.

System-on-Chip Test Data Compression Based on Split-Data Variable Length (SDV) Code

System-on-a-chips with intellectual property cores need a large volume of data for testing. The large volume of test data requires a large testing time and test data memory. Therefore new techniques are needed to optimize the test data volume, decrease the testing time, and conquer the ATE memory limitation for SOC designs. This paper presents a new compression method of testing for intellectual property core-based system-on-chip. The proposed method is based on new split- data variable length (SDV) codes that are designed using the split-options along with identification bits in a string of test data. This paper analyses the reduction of test data volume, testing time, run time, size of memory required in ATE and improvement of compression ratio. Experimental results for ISCAS 85 and ISCAS 89 Benchmark circuits show that SDV codes outperform other compression methods with the best compression ratio for test data compression. The decompression architecture for SDV codes is also presented for decoding the implementations of compressed bits. The proposed scheme shows that SDV codes are accessible to any of the variations in the input test data stream.

VISUALIZATION OF THREE-DIMENSIONAL DATA FIELD AND ITS APPLICATION IN MACHINE TESTING

In order to realize visualization of three-dimensional data field （TDDF） in instrument, two methods of visualization of TDDF and the usual manner of quick graphic and image processing are analyzed. And how to use OpenGL technique and the characteristic of analyzed data to construct a TDDF, the ways of reality processing and interactive processing are described. Then the medium geometric element and a related realistic model are constructed by means of the first algorithm. Models obtained for attaching the third dimension in three-dimensional data field are presented. An example for TDDF realization of machine measuring is provided. The analysis of resultant graphic indicates that the three-dimensional graphics built by the method developed is featured by good reality, fast processing and strong interaction

Effect of Sample Disturbance on Unconfined Compression Strength of Natural Marine Clays

Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.

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.

Initial Stable State of Ocean Floor Deposits

Extensive oedometer tests and physical tests have been conducted on remolded and reconstituted marine soils with different initial water contents and liquid limits. The oedometer test data can be well fitted with a straight line in the bilogarithmic ln(1+ e ) ～ lg p plot. The initial effective stress corresponding to the initial void ratio is determined by extrapolation of the bilogarithmic compression line. This new way of extrapolating oedometer test data to the initial void ratio overcomes the difficulty of measuring the residual effective stress of soils in the remolded state. The initial stable compression line of ocean floor deposits under different deposition environments corresponds to the compression line at the sensitivity equal to one. This initial stable compression line obtained in this study is consistent with the available compression line at the sensitivity equal to one which is proposed based on the experimental data of remolded undrained strength and the theoretical concept of Cam clay critical state line.

A Novel Variable Shifting Code for Test Compression of SoC

The test vector compression is a key technique to reduce IC test time and cost since the explosion of the test data of system on chip （SoC） in recent years. To reduce the bandwidth requirement between the automatic test equipment （ATE） and the CUT （circuit under test） effectively, a novel VSPTIDR （variable shifting prefix-tail identifier reverse） code for test stimulus data compression is designed. The encoding scheme is defined and analyzed in detail, and the decoder is presented and discussed. While the probability of 0 bits in the test set is greater than 0.92, the compression ratio from VSPTIDR code is better than the frequency-directed run-length （FDR） code, which can be proved by theoretical analysis and experiments. And the on-chip area overhead of VSPTIDR decoder is about 15.75 % less than the FDR decoder.

Architecture and Methodology of Unit Testing Embedding Pair-Wise Mode for Small Team

In this paper, the new organization for unit testing embedding pair-wise mode is proposed with the core thought focused on the cooperation of programmer and tester by “cross-testing”. The typical content of unit testing for the new organizing mode should have three aspects, including self-checking, cross-testing and independent-testing. For cross-testing, executing “pair-wise” mode, mainly tackles data testing, function testing and state testing, which function testing must be done by details and state testing must be considered for completeness. With the specializing of independent-testing, it should be taken as more rigid testing without arbitrariness. Consequently, strategy and measure are addressed for data testing focusing on boundary testing and function/state testing. And organizing method of procedure and key points of tackling unit testing are investigated for the new organizing mode. In order to assess the validity of our study and approach, a series of actual examples are demonstrated for GUI software. The result indicates that the execution of unit testing for the new organizing mode is effective and applicable.

Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine

To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.

Generating test data for both path coverage and fault detection using genetic algorithms

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.

On Test Data Compression Using Selective Don＇t-Care Identification

This paper proposes an effective method for reducing test data volume undermultiple scan chain designs. The proposed method is based on reduction of distinct scan vectorsusing selective don't-care identification. Selective don't-care identification is repeatedlyexecuted under condition that each bit of frequent scan vectors is fixed to binary values (0 or 1).Besides, a code extension technique is adopted for improving compression efficiency with keepingdecompressor circuits simple in the manner that the code length for infrequent scan vectors isdesigned as double of that for frequent ones. The effectiveness of the proposed method is shownthrough experiments for ISCAS'89 and ITC'99 benchmark circuits.

A novel strategy for automatic test data generation using soft computing technique

Software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. Software development process in- vests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear struc- ture of software. Moreover, test case type and scope deter- mines the quality of test data. To address this issue, software testing tools should employ intelligence based soft comput- ing techniques like particle swarm optimization （PSO） and genetic algorithm （GA） to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing ex- periments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test ad- equacy criterion as branch coverage. The performance ade- quacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.

Clarifying predictions for COVID-19 from testing data: The example of New York State

With the spread of COVID-19 across the world,a large amount of data on reported cases has become available.We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time.Indeed,tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases.Such a limitation may have a strong impact on the reported cases data.Indeed,some cases may be missing from the official count because the number of tests was not sufficient on a given day.In this work,we propose a new differential equation epidemic model which uses the daily number of tests as an input.We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York.We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases.We obtain a good match between the data and the outcome of the model.Finally,by multiplying the number of tests by 2,5,10,and 100 we explore the consequences for the number of reported cases.

Improved Data Compression Scheme for Multi-Scan Designs

This paper presents an improved test data compression scheme based on a combination of test data compatibility and dictionary for multi-scan designs to reduce test data volume and thus test cost. The proposed method includes two steps. First a drive bit matrix with less columns is generated by the compatibilities between the columns of the initial scan bit matrix, also the inverse compatibilities and the logic dependencies between the columns of mid bit matrixes. Secondly a dictionary bit matrix with limited rows is constructed, which has the properties that for each row of the drive bit matrix, a compatible row exists or can be generated by XOR operation of multiple rows in the dictionary bit matrix and the total number of rows used to compute all compatible rows is minimal. The rows in the dictionary matrix are encoded to further reduce the number of ATE channels and test data volume. Experimental results for the large ISCAS 89 benchmarks show that the proposed method significantly reduces test data volume for multi-scan designs.