A scheme on automated test data generation and its evaluation

By analyzing some existing test data generation methods, a new automated test data generation approach was presented. The linear predicate functions on a given path was directly used to construct a linear constrain system for input variables. Only when the predicate function is nonlinear, does the linear arithmetic representation need to be computed. If the entire predicate functions on the given path are linear, either the desired test data or the guarantee that the path is infeasible can be gotten from the solution of the constrain system. Otherwise, the iterative refining for the input is required to obtain the desired test data. Theoretical analysis and test results show that the approach is simple and effective, and takes less computation. The scheme can also be used to generate path-based test data for the programs with arrays and loops.

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.

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.

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.

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.

Application of Dynamic Slicing in Test Data Generation

The program slicing technique is employed to calculate the current values of the variables at some interest points in software test data generation. This paper introduces the concept of statement domination to represent the multiple nests, and presents a dynamic program slice algorithm based on forward analysis to generate dynamic slices. In the approach, more attention is given to the statement itself or its domination node, so computing program slices is more easy and accurate, especially for those programs with multiple nests. In addition, a case study is discussed to illustrate our algorithm. Experimental results show that the slicing technique can be used in software test data generation to enhance the effectiveness.

Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.

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.

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.