Test Data For Path Coverage Research Articles

Generating Test Data for Path Coverage Based on Genetic Algorithm

Test data generation is an important part of software testing. The imbalance of data crossing program branches is often ignored in generating test data. As a result, there is much data crossing some branches while little data crossing other branches. To solve the phenomenon so as to generate test data effectively, we introduce branch balance and program balance in the evolutionary generation of test data. First, the number of individuals crossing the true and false branch of each branch node on the target path are computed. Then, the calculation methods of branch balance and program balance are given. Finally, the fitness value function which considers the change of program balance before and after an individual joining is presented. And an individual that can improve the balance will be retained in the evolution process. Experiments show that our method is better than the other method in running time and success rate.

Genetic algorithm based test data generation for MPI parallel programs with blocking communication

Parallel computing is one of mainstream techniques for high-performance computation in which MPI parallel programs have gained more and more attention. Genetic algorithms (GAs) have been widely employed in automated test data generation, leading to a major family of search-based software testing techniques. However, previous GA-based methods have limitations when testing MPI parallel programs with blocking communication. In this paper, we focus on the path coverage problem for MPI parallel programs with blocking communication, and formulate the problem as an optimization problem with its decision variable being the program input and the execution order of sending nodes. In addition, we develop target amending strategies for candidates when solving the problem using genetic algorithms. The proposed method is evaluated and compared with several state-of-the-art methods through a series of controlled experiments on five typical programs. The experimental results show that the proposed method can effectively and efficiently generate test data for path coverage.

Test data generation for path coverage of message-passing parallel programs based on co-evolutionary genetic algorithms

Employing genetic algorithms to generate test data for path coverage has been an important method in software testing. Previous work, however, is suitable mainly for serial programs. Automatic test data generation for path coverage of message-passing parallel programs without non-determinacy is investigated in this study by using co-evolutionary genetic algorithms. This problem is first formulated as a single-objective optimization problem, and then a novel co-evolutionary genetic algorithm is proposed to tackle the formulated optimization problem. This method employs the alternate co-evolution of two kinds of populations to generate test data that meet path coverage. The proposed method is applied to seven parallel programs, and compared with the other three methods. The experimental results show that the proposed method has the best success rate and the least number of evaluated individuals and time consumption.

PERFORMANCE ANALYSIS OF TEST DATA GENERATION FOR PATH COVERAGE BASED TESTING USING THREE METAHEURISTIC ALGORITHMS

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the evolved test cases using those meta-heuristic algorithms. Genetic Algorithms, Artificial Bee Colony optimization and Differential Evolution are acting here as meta-heuristic search paradisms for path coverage based test data generation. Finally the performance of the test data generation using three meta-heuristic optimization algorithms are empirically evaluated and compared.

Evolutionary Generation of Test Data for Paths Coverage Based on Scarce Data Capturing

Using genetic algorithms to generate test data for path coverage is a hot topic in software testing automation.The established fitness functions of previous methods cannot provide adequate protection to a scarce datum which covers a node difficult to be covered,so the efficiency of generating test data needs to be improved.In this study,scarce data are dynamically captured during the evolutionary generation of test data.We obtain the contribution of an individual by counting up the number of individuals which traverse each node of the target path,and regard this contribution as a weight to adjust the fitness of the individual.In this way,the fitness of a scarce datum can be increased and the scarce datum can be kept in the subsequent evolution,so the efficiency of generating test data is improved.The proposed method is applied to generate test data for covering paths of two benchmark and six industrial programs,and is compared with traditional and random methods.The experimental results confirm that the proposed method is efficient in generating test data for path coverage.

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 studies 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 evolutionary algorithmis employed to solve the formulatedmodel, and several types of fault detectionmethods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary optimization 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.