We inject only one fault in each page respectively, so at least 7 faults exist totally (there may be other faults in the Web application originally). A set of user sessions are obtained after scanning user logs on the Web server and purging their irrelevant information; then 89 mean- ingful user sessions are finally created after scanning them again. Only 17 user sessions are used to generate test cases after the reduction of the meaningful user ses- sions using the URL trace-based ReduceUSession algo- rithm; and the reduction ratio reaches 80.9%. Imperson- ally, more user sessions there are for a given Web appli- cation, much higher is the reduction efficiency, for more user sessions mean higher possibility that the URL trace requested by a usersession is the prefix of that requested by another in the view of statistics. The set of 17 user sessions † (or test cases) is denoted by Γ1. After grouping
individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Geneticalgorithm (GA) has been widely used in optimization with binary and continuous variable and is popular in solving facility layout problem, R. Haupt and Haupt (2004). GA are found to be efficient search methods for finding solution of Travelling Salesperson problem (Choubey, 2012; Choubey, 2013), Travelling Tournament problem (Choubey, 2010), Solving different maze structures (Choubey, 2012), grammar induction (Choubey and Kharat, 2012; Nitin Choubey and Madan Kharat, 2011; Choubey and Kharat, 2010) etc. Misola and Navarro (2014) presented geneticalgorithm to solve facility layout problem to minimize total cost function (Maricar G. Misola and Bryan B. Navarro, 2014). Dalgic used GA for Market floor planning. The objective of the paper is to find the optimal grid-shelf layout for the floor planning usingGeneticAlgorithm with different level of complexity for the specific rectangular floor dimension.
In a geneticalgorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached.
Abstract: Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and testcase reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, GeneticAlgorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic geneticalgorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT.
The proposed approach reduces the nodular path options by a combined application of bee colony optimization and modified geneticalgorithm. The technique developed using this approach identifies and reduces the test data. This approach provides better results in the initial iteration of the complete process. It provides positive feedback and hence it can lead to superior solutions in optimum time. This technique is particularly focused on the decision nodes functioning which are major determinants of downstream information flow routes. Tools based on such approach will not only reduce the number of test cases for the comprehensive validation of software but also will lead to over all improve the quality of fault managements (specifically during testing phase; which accounts for more than 31% software failures) as per our previous findings of empirical study of root cause analysis of software failure. Future outlook based on current algorithm would be to develop another algorithm for automatic fault rectification.
The proposed Algorithms called GABAOT (Genetic Algorithms based Automatic Optimization techniques) has been tested for data considering 10 subject. The efficiency of Recognizing the face at different age has been kept at a threshold range of 90% to 100% and verified from the trained data stored in the master file. For each face image the geometrical feature measurement have been done with the dataset (FACE_MODEL) and hence employed genetic algorithms for the best fit or match.
Multiple variants of GA have been implemented throughout the years as test cases optimizationalgorithm such as Weight-BasedGeneticAlgorithm (WBGA), Fuzzy-Based Age Extension of GeneticAlgorithm (FAexGA) and Non-Dominated Sorting GeneticAlgorithm (NSGA II). In general, WBGA implements weight on the chromosomes to find the fitness value hence lead to optimizing overall test cases(Wang et al., 2013). Meanwhile, FAexGA purposes to assign aging technique to the test cases to eliminate the old test cases (Last et al., 2006). Each of these algorithms has their own drawback. For example, WBGA cannot become the best solution for testcaseoptimization due to fixed weight applied to the test cases while FAexGA techniques only applicable to GUI testing only. NSGA II on the other hand is sorting the test cases using only crowding distance approach to find the most optimize set of test cases (Jeyaprakash and Alagarsamy, 2015).
Web usage mining is one of the applications of data mining technique.. There are various tools are available for analyzing weblog such as Rapid Miner, Weblog Expert, Deep Log Analyzer etc., and every tool offered some or the other feature which was better than the rest. Web usage mining is one of the prominent research area due to these following reasons . Thus personalization for a user can be achieved through web usage mining. The second is to identify frequent access patterns for the users. Those patterns are to improve the overall performance of future accesses. Common access patterns improve the actual design of web pages and for making other medications to a website   . Usage patterns can be used for business intelligence in order to improve sales and advertisement by providing product recommendations. Web usage mining approach is very efficient for identifying user's behavior in a SNS. Through this approach advertisement recommendation is possible in SNS. There are various ways to predict user behavior such as a particular timestamp, which kind of web services mostly accessed by a user. Identify commonly used browsers by user  . Suggest friend/group in social networking with various constraints.
The most current exploration is being done on testcasegenerationusingGeneticAlgorithm. An optimization technique like GA can be used to solve various problems. It uses existence of the fittest technique, where the best solution survives. GeneticAlgorithm is based on “SURVIVAL OF FITTEST”. In geneticalgorithm we represent input by set of chromosomes. These chromosomes are represented in different numbers in computer. The mostly used representation of chromosomes is binary representation. Basic steps of geneticalgorithm are shown in fig2.1 :-
The key objective of this paper is comparative evaluation of testcasegeneration and optimization for two bio-inspired algorithms GeneticAlgorithm and Ant Colony Algorithm. These Search Optimization techniques provide the best solution. These algorithms are used to generate test paths and then optimize them. The case study is being presented using Activity Diagram on Airline Reservation System by applying both Optimization Algorithms. Activity Diagram is transformed into Activity Graph. The Nodes of the graph show a test path which is being optimized usingGeneticAlgorithm and Ant Colony Optimization.The study done is measured in terms of number of iterations and execution speed. The empirical results prove that the algorithm for Ant Colony Optimization shows better results as compared to GeneticAlgorithm. The proposed technique gives the comparative results of bio-inspired Algorithms. The two Algorithms can be combined to get better optimization results. The proposed technique can be used to develop automated tool.
Ahmet Ozkis et al.(2013) proposed Accelerated ABC (A- ABC) Algorithm for Continuous Optimization Problems. In A-ABC, two modifications are used on the Artificial Bee Colony (ABC) algorithm to progress its local search ability and convergence speed. Sandeep Kumar et al. (2013) introduced Crossover based ABC (CbABC) which integrates crossover operation from GeneticAlgorithm (GA) with original ABC algorithm. The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. Xiangyu Yu et al.(2013) introduced a sensor deployment algorithmbased on modified ABC algorithm called FNF, (forgetting and neighbor factor)-BL (backward learning) ABC algorithm is proposed. In order to have a better coverage and a faster convergence speed, the onlooker bee phase and the scout bee phase of the ABC algorithm have been modified. Takeshi Nishida (2013) modified the ABC algorithm for adaptation to time-varying functions. To adjust to the change in the function, a procedure for re-evaluating the bees at each time is introduced. Dharmender Kumar et al. (2013) proposed a variant of ABC algorithmbased on rectangular topology structure, namely rectangular topology based Artificial Bee Colony algorithm (RABC). RABC significantly improves the original ABC in solving high dimensional optimization problems. Alina Rakhi Ajayan et al. (2013) proposed an improved ABC algorithm to match the different characteristics of wireless sensor network deployment process, which will be optimum for real time dynamic network functioning. Muhammad shahrizan shahrudin et al (2013) presented a modified ABC algorithm to fine optimum value for optimum functions. The Hybrid ABC algorithm are applied searching mechanisms and probability functions. Ivona Brajevic et al. (2013) introduced an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm.
V. CONCLUSION AND FUTURE WORK Software testing is necessary to validate the requirements and to maintain software quality. The comparison of DFS and BFS algorithm to produce automatic test cases for AD and SD has been presented in this paper. The suitable test cases are produced from both algorithms. The test cases which are produced according to AD, SD, and STG graphs are given. The activity diagram based coverage criterion is considered for generation of test cases. But the test cases cannot display the whole process like lack of information in filling of application during the testing. So it becomes onerous to process further because of not using of standards words in the diagram. The test cases which are produced from SD only screen the system section. But the produced test cases display the whole information of business process inside a system. The test cases produced from the integrated graph shows the features of AD and SD. But consist of unnecessary information. The optimization of test cases by usinggeneticalgorithm will be combined with the current work.
Abstract- The existing testing approaches for web application testing can takes long time to generate test suites. So that the UserSessionBased Testing has introduced which is depends on capturing and replaying user sessions of a website. It is an effective testing methodology for web applications. Previous studies have exposed that the testing suites generated by usersessionbased testing frequently find the faults neglected by other testing approaches. This paper addresses the problem and discussing different techniques and methodologies have been proposed for taking care of their issues. GeneticAlgorithm (GA) is one such form of evolutionary algorithms which is the area of research. In this paper we discuss the algorithms based on geneticalgorithm and in future we will try to resolve its problem. Index Terms- Web application testing, Usersessionbased testing, Web server, Session log
• Also, modeling the techniques is a difficult task. These limitations urge the researchers to implement metaheuristic techniques in application domains. Schedul- ing problems are proved to be NP-hard types of problems and are not easily or exactly solved for large sizes. The ap- plication of metaheuristics technique to solve such NP hard problems needs attention. A futile effort that this paper will not pursue is to overstate the capabilities of the optimiza- tion methods discussed herein. Rather, the objective is to present how population-based meta-heuristic techniques work and indicate their applications. This paper goes on to present, in the following sections, genetic algorithms, ant colony optimization, particle swarm optimization, simulat- ed annealing, differential evolution, and teaching-learn- ing-basedoptimization, and the conclusion.
Maximizing operating profits includessearching and adopting strategies to maximizeyield from customers. Simple linear programming models of revenue optimization for airline and hotel are presented and their solution through geneticalgorithm is proposed and analysed in this dissertation. Exploringthe properties of GAs to allow procedure-basedfunction declarations and coding of variables reducesthe complexity of the problem. The GA- based modelwas tested with various hypothetical cases. The results observed are consistentwith expectations. The results are also compared using different selection and crossover combinations. It is found that each behaves in a different manner depending on the problem.However, there are several limitations in the problemformulation that warrant further investigation andanalysis.
Nayak and Mohapatra  used PSO for the generation of test suite for structural testing using data flow testing. This new approach is tested on the 14 FORTRAN programs while considering all uses criterion as test adequacy criteria. The results achieved after simulation of the suggested approach on MATLAB are compared with the solution achieved using GA algorithm. Using an empirical study of results achieved after experiments, a conclusion is derived that PSO performs much better than GA in achieving 100% def-use coverage. In other words, PSO achieved the same results in lesser number of generation compared to GA. Agarwal and Srivastava used a modified PSO algorithm, called Discrete Quantum Particle Swarm Optimization (QPSO), to generate test cases automatically for three benchmark programs e.g. triangle classifier, calculation of number of days between two given dates and line in a rectangle problem. The branch coverage is considered as test adequacy criteria and fitness function is also based on branch predicates in branching condition. Mao  also used PSO for the generation of test suite while considering a objective function depending on branch coverage and branch distance for the guidance of the PSO in its search space. An empirical study is performed after finding test cases using PSO for eight benchmark programs and test cases produced by Simulated Annealing (SA) and Geneticalgorithm on matrices like average percentage coverage, success rate, average generations and average execution time. In the end, this study concludes that PSO performs better than SA and GA. Jiang et al.  used a modified PSO algorithm called Reduced Adaptive PSO, for automatic test data generation. This suggested algorithm modifies the evolution equation after removing the velocity component of PSO and considers only inertia weight. This
The paper presents a new interconnect testgeneration scheme based on adaptive geneticalgorithm (AGA) and particle swarm optimizationalgorithm (PSO) for Multi-chip Module (MCM) applications. By combing the characteristics of interconnect test and constructing particle expression of testgeneration, the velocity updating equation and position updating equation of discrete PSO are presented in this paper. AGA generates the initial candidate test vectors in this scheme. In order to improve the fault coverage of the test vector, PSO is employed to evolve the candidates generated by AGA. The international standard MCM benchmark circuit was used to verify the scheme. Comparing with not only the evolutionary algorithms, but also the deterministic algorithms, simulation results demonstrate that the hybrid scheme can achieve high fault coverage, short CPU time and compact test set, which shows that it is a novel optimized method deserving research.
IJEDR1502107 International Journal of Engineering Development and Research (www.ijedr.org) 585 required parameters. The failure data is generated and required parameters are estimated with Cuckoo Search. The idea behind using Cuckoo Search is its properties such as robustness, efficiency and effectiveness. Srivastava et. al. discussed the importance and demand of structural testing in software testing for code-based criteria and presented an efficient method to generate all the paths possible in a Control Flow Graph required for Path Testing. The optimal paths are identified through Cuckoo Search algorithm which is equal to the cyclomatic complexity of program under test. Iqbal et. al. considered the problem of testcasegeneration as a problem of multi objective optimization which is having two objectives which has to be fulfilled simultaneously. The two discussed objectives are Path Priority and Oracle Cost. For effective Testing to take place in real time many constraints have to be fulfilled. For the satisfaction of the generation of test cases the multiple objectives reduces the overall efforts of testing. MOFA technique which is Multi Objective Firefly Algorithm is used to solve such kind of problems. Sudhir et. al. reduced the regression testing cost by scheduling the test cases according to the testcase prioritization techniques and maximized some objective function. TestCase prioritization takes place according to the test cases importance in some criteria and prioritized test cases are first tested in regression Testing. The applicability of objective function are on the basis of the rapid discovered faults during the process of testing. Evaluation of the techniques for testcase prioritization is discussed with Average Percentage of Faults Detected) metric. Srivatsava et.al. presented an approach on Firefly Algorithm which is a meta heuristic technique for the generation of optimal paths. The algorithm is modified by the different definition of objective function and introduction of guidance matrix for path traversal in the graph.
The most striking feature of SDLC is software testing. It is very labour-intensive and expensive process in software development and handling as well as maintenance of software. The main objective of this paper is to extend the testing technique. Testing is to show the incorrectness and is considered to succeed when an error is detected [Myers79]. Today’s automatic testing has replaced manual testing with a great extent. Automating testing is very helpful in reducing human efforts to generate test cases or test data. Test data or testcase is a very tiresome task in software testing. It has multiple set of values or data that are used to test the functionality of a particular feature. All degrees of the test values and conditions maintained in separate files and stored as test data. Testcase or data generation is a set of conditions or rules that are developed for finding the failure points in a developing software. Nowadays, many researches have paid considerable attention, focusing on test data generation techniques. This paper adopts a case study and proposes a technique for test data generation, based on geneticalgorithmusing critical path. Critical path testing is considered to solve the looping problem and improving the testing efficiency. Test data scenario is derived from sequence diagram. Sequence diagram reveals the sequence of calls in a system using exchange of messages among the objects of system.