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Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary...
Genetic Algorithms are heuristic approach for forming the bases of search based algorithms. It applies the mechanism of the natural selection of genes & the phenomena's associated with the genetics like mutation, crossover, and replication to provide solutions in some complex searches. In this paper, we have reviewed their applications in context of the Object oriented paradigm thus proving their...
To solve the problem that it is difficult to construct an exact mathematic model for the electro-hydraulic position servo system of a pump-controlled cylinder with nonlinearity and time-varying property, HHGA-RBFNN is proposed. Each chromosome only contains three parameters including the number of hidden nodes, center and width of radial basis function; so that the complexity of proposed algorithm...
This paper discusses the use of Species per Path approach [1] and gene expression messy genetic algorithm (GEMGA) for automatic software test data generation. This research finds another path problem and extends Species per Path approach on dynamic test data generation. In increasing the search space by program transformation for the path potentially suffering from the path problem, this research...
Our experience with applying model-based testing on industrial systems showed that the generated test suites are often too large and costly to execute given project deadlines and the limited resources for system testing on real platforms. In such industrial contexts, it is often the case that only a small subset of test cases can be run. In previous work, we proposed novel test case selection techniques...
We propose a new method to evaluate individuals in genetic algorithms (GAs) for algorithmic trading in stock markets. In our previous work, we presented an effective method to acquire trading strategy in stock markets. However, it had a tendency of overfitting in genetic searches. Our new approach, namely neighborhood evaluation, involves evaluation for neighboring points of genetic individuals in...
Applying model-based testing (MBT) in practice requires practical solutions for scaling up to large industrial systems. One challenge that we have faced while applying MBT was the generation of test suites that were too large to be practical, even for simple coverage criteria. The goal of test case selection techniques is to select a subset of the generated test suite that satisfies resource constraints...
Pairwise test set generation is the process of producing a subset of all possible test case inputs to a system in situations where exhaustive testing is not possible or is prohibitively expensive. For a given system under test with a set of input parameters where each parameter can take on one of a discrete set of values, a pairwise test set consists of a collection of vectors which capture all possible...
This paper describes the technique dedicated to an analog integrated circuit testing by means of supply current monitoring. The minimal set of test points, that allows to achieve the highest possible fault coverage, is determined with the use of genetic algorithm. Thanks to the proposed dynamic scheme of phenotype coding, the optimization process is more efficient than for a standard, static genotype...
Feature selection is the basic of the state evaluation and fault diagnosis, and it is difficult to get the reasonable token feature. In the paper, an intelligence method of state feature system establishment and optimize has been studied. Diesel engine was illustrated in the paper, the signal of diesel engine has been collected when the piston ring and airtight ring working at different state, then...
In e-learning environment, Web-based testing system can help to evaluate learners learning status precisely. To meet the needs of multiple assessment criteria, a new test-sheet construction model was proposed. Based on the proposed model, a differential evolution algorithm with effective coding strategy was designed to generate high quality test-sheets for Web- based testing. In simulation experiments,...
An evolutionary neural network modeling approach for software cumulative failure prediction based on feed-forward neural network is proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training a neural network established by Aljahdali S.. In this paper we present a real coded genetic algorithm that uses the appropriate operators for this encoding type...
Intellectual property infringement and plagiarism litigation involving source code would be more easily resolved using code authorship identification tools. Previous efforts in this area have demonstrated the potential of determining the authorship of a disputed piece of source code automatically. This was achieved by using source code metrics to build a database of developer profiles, thus characterizing...
An evolutionary regression modeling approach for software cumulative failure prediction based on auto-regression order 4, 7 and 10 models are proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training the auto-regression model. In this paper, we present a real coded genetic algorithm that uses the appropriate operators for this encoding type to train...
Most complex systems nowadays heavily rely on software, and spacecraft and satellite systems are no exception. Moreover as systems capabilities increase, the corresponding software required to integrate and address system tasks becomes more complex. Hence, in order to guarantee a system's success, testing of the software becomes imperative. Traditionally exhaustive testing of all possible behaviors...
Achieving high structural coverage such as branch coverage in object-oriented programs is an important and yet challenging goal due to two main challenges. First, some branches involve complex program logics and generating tests to cover them requires deep knowledge of the program structure and semantics. Second, covering some branches requires special method sequences to lead the receiver object...
The clonal selection (CS) algorithm is an optimization algorithm based upon the clonal selection principle in the biological immune system. This paper presents a novel approach that uses CS algorithm for path-oriented test data generation. The approach takes a selected path as a target and executes sequences of operators iteratively for test case to generate. An affinity function which is made up...
This paper proposes an approach to solve the triangle decision tree problem for computer adaptive testing (CAT) using genetic algorithms (GAs). In this approach, item response theory (IRT) parameters composed of discrimination, difficulty, and guess are firstly obtained and stored in an item bank. Then a fitness function, which is based on IRT parameters, of GAs for obtaining an optimal solution is...
Test time optimization is necessary for modular testing of hierarchical system-on-chip (SOC) that contain embedded IP core. In this paper, we consider the case of non-interactive design transfer between IP core vendor and IC integrator. We proposes a method based on genetic algorithm which can efficiently optimize the test time of hierarchical SOC. Utilizing international reference circuit provided...
Genetic algorithms have been successfully applied in the area of software testing. The demand for automation of test case generation in object oriented software testing is increasing. Genetic algorithms are well applied in procedural software testing but a little has been done in testing of object oriented software. In this paper, we propose a method to generate test cases for classes in object oriented...
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