Comparative Evaluation of A Maximization And Minimization Approach for Test Data Generation with Genetic Algorithm and Binary Particle Swarm Optimization

In search-based structural testing, metaheuristic search techniques have been frequently used to automate the test data generation. In Genetic Algorithms (GAs) for example, test data are rewarded on the basis of an objective function that represents generally the number of statements or branches covered. However, owing to the wide diversity of possible test data values, it is hard to find the set of test data that can satisfy a specific coverage criterion. In this paper, we introduce the use of Novelty Search (NS) algorithm to the test data generation problem based on statement-covered criteria. We believe that such approach to test data generation is attractive because it allows the exploration of the huge space of test data within the input domain. In this approach, we seek to explore the search space without regard to any objectives. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far.

Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.

This paper aims at incorporating immune operators in genetic algorithms as an advanced method for solving the problem of test data generation. The new proposed hybrid algorithm is called immune genetic algorithm (IGA). A full description of this algorithm is presented before investigating its application in the context of software test data generation using some benchmark programs. Moreover, the algorithm is compared with other evolutionary algorithms.

T-way testing is a sampling approach for test data generation. Recently, adapting meta-heuristic algorithms for t-way testing is very attractive in order to find a minimum subset of test data that can test a system overall. As a consequence, several meta-heuristic algorithms have been used as the basis of t-way strategies. In order to guide software tester (and engineers in general) to select the best algorithm for the problem at hand, there is a need to evaluate and benchmark the performance of each strategy against common case studies. This paper presents a comparative study between two meta-heuristic strategies for t-way test data generation: Flower Pollination Algorithm (FPA) and Cuckoo Search (CS). Our experiments have performed on a real-world case study. Experimental results demonstrate that FPA appears to produce better results in most of the test cases in term of test suite size and convergence rate owing to its ability for controlling local and global search.

Test data generation is one of the most technically challenging steps of testing software, but most commercial systems currently incorporate very little automation for this step. This paper presents results from a project that is trying to find ways to incorporate test data generation into practical test processes. The results include a new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint-based testing, and dynamic test data generation. It takes an initial set of values for each input, and dynamically ‘pushes’ the values through the control-flow graph of the program, modifying the sets of values as branches in the program are taken. The result is usually a set of values for each input parameter that has the property that any choice from the sets will cause the path to be traversed. This procedure uses new analysis techniques, offers improvements over previous research results in constraint-based testing, and combines several steps into one coherent process. The dynamic nature of this procedure yields several benefits. Moving through the control flow graph dynamically allows path constraints to be resolved immediately, which is more efficient both in space and time, and more often successful than constraint-based testing. This new procedure also incorporates an intelligent search technique based on bisection. The dynamic nature of this procedure also allows certain improvements to be made in the handling of arrays, loops, and expressions; language features that are traditionally difficult to handle in test data generation systems. The paper presents the test data generation procedure, examples to explain the working of the procedure, and results from a proof-of-concept implementation. Copyright © 1999 John Wiley & Sons, Ltd.

The problem of test data generation in software testing is well referenced. Of the various methods used in the literature, symbolic execution appears to be a promising approach. It can be used either for software verification or to facilitate the automated test data generation process. A number of symbolic systems that employ symbolic execution to generate test data have already been constructed. However, these systems analyze programs written in specific programming languages. Thus, although some of them use an internal representation for performing symbolic execution, each one can only deal with programs in a single language. In this paper a script language called SYMEXLAN is presented. It can be adapted in order to construct a general symbolic execution testing system independent of the language in which the software under test is written.

Bayesian Network is a knowledge representation formalism that has been proven to be valuable in gene regulatory network reconstruction. However, it is showed that the structure learning of Bayesian networks is an NP-hard problem. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, K2 algorithm has been found to be the most successful. However, the performance of K2 algorithm is greatly affected by a prior ordering of input nodes. If using inappropriate orderings, the accuracy of the learned structures will be relatively low. This paper represents a new structure learning method to reconstruct gene networks from the observational gene expression data. The proposed method is based on Binary Particle Swarm Optimization and K2 algorithm. It firstly identifies nodes ordering from the experimental data by using Binary Particle Swarm Optimization algorithm. Then, the nodes ordering information is used as an input of K2 algorithm so as to improve the performance and efficiency of the K2 algorithm in determining the network structure. Our method is evaluated using two benchmark network datasets with known structures. Experimental studies show that the proposed method can identify networks more efficiently and accurately than comparable methods reported in the literature.

This research studies on the performance of Binary Particle Swarm Optimization (BPSO) algorithm. The first part is finding the best inertia weight of BPSO from various types of inertia weight. The second part is optimizing the cross-sectional area of steel structures and topology of bracing system under vertical and lateral load. The structures studied in the research include unbraced frames and X-braced frames. Moreover, the braced frame also investigates the influence of the classification groups of elements. The elements are classified into finer groups than the original group. The design of the structure follows the AISC code. From the investigation in the first part, a constant inertia weight of 0.98 is the best. In the second part, minimum weights of unbraced frames using BPSO are the lowest weight, except three-bays, twenty-four stories frame. For braced frames with original grouping, all examples get a lower weight than the unbraced frames. For studying the influence of group, the results of the two examples are contradictory. One bay, ten stories frame with new group has a minimum weight less than the original while three-bays but twenty-four stories frame is opposite.

Here, we suggest the possibility of optical circuit design approach by employing the binary optimization of plasmonic nano rods. The proposed mechanism is based on combination of binary particle swarm optimization (BPSO) algorithm and discrete dipole approximation method. BPSO, a group of birds including a matrix with binary entries responsible for controlling nano rods in the array, shows the presence with symbol of (‘1’) and the absence with (‘0’). The current research represents a nanoscale and compact four channels plasmonic Demultiplexer as optical circuit. It includes eight coherent perfect absorption (CPA)—type filters. The operation principle is based on the absorbable formation of a conductive path in the dielectric layer of a plasmonic nano-rods waveguide. Since the CPA efficiency depends strongly on the number of plasmonic nano-rods and the nano rods location, an efficient binary optimization method based the BPSO algorithm is used to design an optimized array of the plasmonic nano-rod in order to achieve the maximum absorption coefficient in the ‘off’ state.

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

Abstract: BCI (Brain-Computer Interface) is a system that transforms a subject’s brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject’s limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.Keywords: brain-computer interface, binary particle swarm optimization, genetic algorithm, support vector machine

A study on the development of a microwave tomography imaging algorithm for detecting the malignant tumor in the breast is presented. Tomography modality is based on the electromagnetic reflections generated by the dielectric contrast between breast tissue types at microwave frequencies. In the tomography method, finite difference time domain method (FDTD) has been used as a technique for calculating electromagnetic scattered fields. In this paper, we propose a novel hybrid optimization technique for solving the inverse scattering problem which uses the binary Genetic algorithm (BGA) and binary particle swarm optimization (BPSO). The convergence rate of this proposed algorithm is around 4 times better than the regular BGA. The proposed FDTD/hybrid BGA-BPSO method has the ability to reconstruct the heterogeneous and dispersive breast tissues to provide a quantitative image of permittivity and conductivity profile of the breast. The proposed technique is capable to detect the size, location and permittivity and conductivity of the tumor even though it is surrounded by benign and fibroglandular tissues.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Cross-project fault prediction (CPFP) is a current field of research in the realm of software engineering. CPFP comes into play when there is a scarcity of within-project training data. In particular, it involves constructing a fault prediction model for software project ‘X’ using the defect/fault data of software project ‘Y’. However, the distribution dissimilarity between the two project's data creates a bottleneck in its success. Many existing approaches addressed this issue by selecting relevant instances from the training data without giving any attention to feature selection (FS). Thus, to assess the power of FS for effective CPFP, we investigated two search-based FS algorithms namely Binary Genetic Algorithm (BGA) and Binary Particle Swarm Optimization (BPSO) algorithm. We performed 26 CPFP experiments based on 8 software projects and compared their performance with a CPFP model (ALL_CPFP), built with all features. Although both BPSO _CPFP and BGA _CPFP showed their potential over ALL_CPFP, BPSO_CPFP performed better than BGA_CPFP in capturing the important features for effective CPFP.

Underground coal mines are susceptible to many kinds of hazards. Mine fire is one of the most vulnerable threats that leads to loss of human lives and other resources inside the mines. Hence, continuous monitoring of underground coal mines is very important for early detection of mine fire. Nowadays, wireless sensor networks are being widely used for monitoring underground coal mines. However, the collected data from sensor nodes are inaccurate in nature, and there is a need for analyzing such data for taking preventive measure. In this article, we propose an optimized fuzzy logic-based fire monitoring system for wireless underground sensor network, which strengthens reliability of making decision in preventing mine fire. However, storing of large fuzzy rule set in the memory constraint sensor nodes and their traversal for every sensor reading are very challenging. Therefore, we use the binary particle swarm optimization (BPSO) algorithm to optimize the proposed fuzzy system that eliminates redundant rules but preserves event detection accuracy of the monitoring system. The proposed system is simulated using BPSO algorithm and Fuzzy Logic Toolbox inbuilt in the MATLAB. The simulation results demonstrate that the proposed system outperforms the existing monitoring systems for underground coal mines.