Application of Genetic Algorithms in Software Testing

Testing object-oriented program is quite challenging task due to the nature of its features like inheritance and polymorphism. In practice test case generation is the most laborious and resource consuming process in software testing hence generating test data for object-oriented programs is even more challenging and effort demanding. Object-oriented evolutionary testing aims at automating test case generation process using evolutionary strategies like Genetic Algorithm. Evolutionary testing gives a helping hand to the software testers to speed up the process and to reduce the amount of project resources. The existing approaches provide sound platform to the researchers to take the work further to meet the challenges that object-oriented paradigm has introduced. One of the limitations in existing approaches is that they combine branch distance of object's state variables with local variables that leaves no guidance for the search process whether object has gained desired state or not. We propose a state-based fitness function for the evolutionary testing of object-oriented programs that can solve object's state problem by evaluating object's state as an independent segment of overall test case fitness. Our initial experiments show that by separating object's state evaluation, search gets better guidance to prevent object's state problem.

The correctness of mission-critical software is an important part of information security, but oracle problem and test data generation are constraints for some programs. Although metamorphic testing (MT) is practical for programs with oracle problem and evolutionary testing (ET) is a good application of genetic algorithm (GA) for automatic test data generation, fitness functions used in ET are not always effective at target search. This article provides a method for improving ET's efficiency by considering metamorphic relation (MR) when fitness function is constructed, and finally some conclusions are presented.

The work on mutation testing has attracted a lot of attention during the last decades. Mutation testing is a powerful mechanism to improve the quality of test suites based on the injection of syntactic changes into the code of the original program. Several studies have focused on reducing the high computational cost of applying this technique and increasing its efficiency. Only some of them have tried to do it through the application of genetic algorithms. Genetic algorithms can guide through the generation of a reduced subset of mutants without significant loss of information. In this paper, we analyse recent advances in mutation testing that contribute to reduce the cost associated to this technique and propose to apply them for addressing current drawbacks in Evolutionary Mutation Testing (EMT), a genetic algorithm based technique with promising experimental results so far.

A genetic algorithm for online-scheduling of a multiproduct polymer batch plant

Application of genetic algorithm in designing high-voltage open-air substation lightning protection system

Conventional software testing is time consuming and expensive task. The techniques for automated test case generation of software is very important as it can reduce the time and cost of this process. Search based test data generation received lot of attention in last two decades for solving this problem. The well known metaheuristic search technique genetic algorithm is used to obtain a specific coverage in software testing. This technique automatically generates test data in order to obtain branch coverage in software testing. The authors first use a technique to identify the unique paths from a control flow graph and then apply genetic algorithm for test case generation. This paper defines the basic research issues of genetic algorithm like chromosome, fitness function, crossing over, mutation in this problem domain and uses a case study to illustrate it.

This paper innovates, within the context of preventive maintenance planning, the application of Genetic Algorithm, as a modern powerful optimization tool, in minimum downtime strategies, for optimal part replacement. Therefore, a brief account of Genetic Algorithm is given and a computer program is written to aid the solution of such problems by Genetic Algorithm. Then, the program is used to solve a numerical example, concerning an equipment with Gaussian failure characteristics. The results obtained by Genetic Algorithm and the ordinary methods are compared to show that exactly the same results can be approached by the application of Genetic Algorithm. Genetic Algorithm and maintenance planning, both being subjects of statistical nature, can be combined to provide a vast area of interesting research work and, hence, some guidelines are suggested for further research.

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Genetic algorithm is one of the optimization techniques, successfully applied in nuclear reactor environment for a variety of applications. Nuclear fuel management optimization is a classical nuclear engineering problem, which has been studied extensively and several techniques, including genetic algorithms, have been used for its solution. The study presented in this paper addresses the overall procedures and methods developed for the application of genetic algorithms in the optimization studies of nuclear fuel management. The result obtained from a study that explores a typical application of genetic algorithm in nuclear fuel management is also presented. Finding out of optimal number of fuel subassemblies in the core of a nuclear reactor (having power generating capacity of 500 MWe) is taken up for the study. The results demonstrate the suitability and efficiency of the algorithm in generating feasible solutions for the selected optimization problem.

The application of genetic algorithms in behavioural ecology, illustrated with a model of anti-predator vigilance

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

Experimental design for fermentation media development: Statistical design or global random search?

Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970's as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.

Genetic algorithms to solve the cover printing problem