Survey on Mutation-based Test Data Generation

The critical activity of testing is the systematic selection of suitable test cases, which be able to reveal highly the faults. Therefore, mutation coverage is an effective criterion for generating test data. Since the test data generation process is very labor intensive, time-consuming and error-prone when done manually, the automation of this process is highly aspired. The researches about automatic test data generation contributed a set of tools, approaches, development and empirical results. In this paper, we will analyse and conduct a comprehensive survey on generating test data based on mutation. The paper also analyses the trends in this field

Parallel Mutant Execution Techniques in Mutation Testing Process for Simulink Models, Journal of Telecommunications and Information Technology, 2017, nr 4

Mutation testing – a fault-based technique for software testing – is a computationally expensive approach. One of the powerful methods to improve the performance of mutation without reducing effectiveness is to employ parallel processing, where mutants and tests are executed in parallel. This approach reduces the total time needed to accomplish the mutation analysis. This paper proposes three strategies for parallel execution of mutants on multicore machines using the Parallel Computing Toolbox (PCT) with the Matlab Distributed Computing Server. It aims to demonstrate that the computationally intensive software testing schemes, such as mutation, can be facilitated by using parallel processing. The experiments were carried out on eight different Simulink models. The results represented the efficiency of the proposed approaches in terms of execution time during the testing process

Seeding Strategies for Multi-Objective Test Case Selection: An Application on Simulation-based Testing

The time it takes software systems to be tested is usually long. This is often caused by the time it takes the entire test suite to be executed. To optimize this, regression test selection approaches have allowed for improvements to the cost-effectiveness of verification and validation activities in the software industry. In this area, multi-objective algorithms have played a key role in selecting the appropriate subset of test cases from the entire test suite. In this paper, we propose a set of seeding strategies for the test case selection problem that generate the initial population of multi-objective algorithms.We integrated these seeding strategies with an NSGA-II algorithm for solving the test case selection problem in the context of simulation-based testing. We evaluated the strategies with six case studies and a total of 21 fitness combinations for each case study (i.e., a total of 126 problems). Our evaluation suggests that these strategies are indeed helpful for solving the multi-objective test case selection problem. In fact, two of the proposed seeding strategies outperformed the NSGA-II algorithm without seeding population with statistical significance for 92.8 and 96% of the problems

APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods

APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION

Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN) optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging