Moheb R. Girgis

Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage

This paper presents an automatic test-data generation technique that uses a genetic algorithm (GA) to generate test data that satisfy data-flow coverage criteria. The technique applies the concepts of dominance relations between nodes to define a new multi-objective fitness function to evaluate the generated test data. The paper also presents the results of a set of empirical studies conducted on a set of programs that evaluate the effectiveness of our technique compared to the random-testing technique. The studies show the effective of our technique in achieving coverage of the test requirements, and in reducing the size of test suites, the search time, and the number of iterations required to satisfy the data-flow criteria.

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. Multiple gateways are needed, which take time and cost budget to set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. This paper concentrates on the challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

A robust method for partial deformed fingerprints verification using genetic algorithm

Fingerprint verification is a well-researched problem, and automatic fingerprint verification techniques have been successfully adapted to both civilian and forensic applications for many years. However, this technology suffers from the problem of handling incomplete prints and often discards any partial fingerprints obtained. Recent research has begun to delve into the problems of incomplete or partial fingerprints. Genetic algorithm is developed to improvement of deformed ridges and complex distortions in fingerprints verification system. In order to deal with low quality fingerprint images. Experimental results demonstrate the robustness of our algorithm to other methods. And results show that the speed is raised using this method in the overall of the most optimum.

Automatic Generation of Data Flow Test Paths using a Genetic Algorithm

testing a program involves generating all paths through the program, and finding a set of program inputs that will execute every path. Since it is impossible to cover all paths in a program, path testing can be relaxed by selecting a subset of all executable paths that fulfill a certain path selection criterion and finding test data to cover it. The automatic generation of such test paths leads to more test coverage paths thus resulting in efficient and effective testing strategy. This paper presents a structural-oriented technique that uses a genetic algorithm (GA) for automatic generation of a set of test paths that cover the all-uses criterion. In the case of programs that have loops, the proposed technique generates paths according to the ZOT-subset criterion, which requires paths that traverse loops zero, one and two times. The proposed GA uses a binary vector as a chromosome to represent the edges in the DD-graph of the program under test. The set of paths generated by the proposed GA can be passed to a test data generation tool to find program inputs that will execute them. Experiments have been carried out to evaluate the effectiveness of the proposed GA compared to the random test path generation technique.

FiVaTech: Page-Level Web Data Extraction from Template Pages

In this paper, we proposed a new approach, called FiVaTech for the problem of Web data extraction. FiVaTech is a page-level data extraction system which deduces the data schema and templates for the input pages generated from a CGI program. FiVaTech uses tree templates to model the generation of dynamic Web pages. FiVaTech can deduce the schema and templates for each individual Deep Web site, which contains either singleton or multiple data records in one Web page. FiVaTech applies tree matching, tree alignment, and mining techniques to achieve the challenging task. The experiments show an encouraging result for the test pages used in many state-of-the-art Web data extraction works.

A tool for computing computer network reliability

The computation of the reliability of a computer network is one of the important tasks of evaluating its performance. The idea of minimal paths can be used to determine the network reliability. This paper presents an algorithm for finding the minimal paths of a given network in terms of its links. Then, it presents an algorithm for calculating the reliability of the network in terms of the probabilities of success of the links of its minimal paths. The algorithm is based on a relation that uses the probabilities of the unions of the minimal paths of the network to obtain the network reliability. Also, the paper describes a tool that has been built for calculating the reliability of a given network. The tool has two main phases: the minimal paths generation phase, and the reliability computation phase. The first phase accepts the links of the network and their probabilities, then implements the first proposed algorithm to determine its minimal paths. The second phase implements the second proposed algorithm to calculate the network reliability. The results of using the tool to calculate the reliability of an example network are given.

Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

ABSTRACT Path testing requires generating all paths through the program to be tested, and finding a set of program inputs that will execute every path. The number of possible paths in programs containing loops is infinite, and so it is very difficult, if not impossible, to test all of them. Path testing can be relaxed by selecting a subset of all executable paths that fulfill a certain path selection criterion and finding test data to cover it. The automatic generation of such test paths leads to more test coverage paths thus resulting in efficient and effective testing strategy. This paper presents a genetical swarm optimization (GSO) based technique, which effectively combines a genetic algorithm (GA) based technique and a particle swarm optimization (PSO) based technique, for automatic generation of a set of test paths that cover the all-uses criterion. Experiments have been carried out to evaluate the effectiveness of the proposed GSO approach in test paths generation compared to the GA and PSO approaches.

Higher order mutation testing: A Systematic Literature Review

Abstract Mutation testing is the process whereby a fault is deliberately inserted into a software system, in order to assess the quality of test data, in terms of its ability to find this fault. Mutation testing is also used as a way to drive the test data development process. Traditionally, faults were inserted one by one into a software system, but more recently there has been an upsurge of interest by the area of higher-order mutation, in which multiple faults are inserted into the system at once. Originally, this was thought to be too expensive, as there was already a concern that the size of the pool of mutants for traditional mutation was already too large to handle. However, following a seminal publication in 2008, it was realized that the space of higher-order mutants (HOMs) could be searched for useful mutants that drive testing harder, and to reduce the overall test effort, by clever combination of first-order mutants. As a result, many authors examined the way in which HOM testing could find subtle hard to kill faults, capture partial fault masking, reduce equivalent mutants problem, reduce test effort while increasing effectiveness, and capture more realistic faults than those captured by simple insertion of first-order mutants. Because of the upsurge of interest in the previous issues, this paper presents the first Systematic Literature Review research specifically targeted at a higher-order mutation. This Systematic Literature Review analyzes the results of more than one hundred sixty research articles in this area. The current paper presents qualitative results and bibliometric analysis for the surveyed articles. In addition, it augments these results with scientific findings and quantitative results from the primary literature. As a result of this work, this SLR presents an outline for many future work.

Face identification system in video

This paper provides a simple and efficient method to detect human faces in videos and recognize persons within the video according to a preset database of known persons. The proposed system consists of three main steps. The first step is skin-like regions detection in CIE-Luv color space. The second step is face detection based on skin-like regions, contour detection and geometrical properties such as face shape. The third step is face verification, in which, each face is compared with a gallery of known faces and the location of the best matched one is returned. Face verification step uses variance formula and skin to non-skin percentage in each facial feature to compare the test face and the faces images in the known database. The proposed system was tested on many different videos with different number of persons in the video. The detected faces are compared with a preset database of known images. Experimental results show that the proposed system is efficient enough to detect faces in different lighting conditions, head pose and face expressions. The results of verification step show that the proposed system is able to retrieve faces from a database with good accuracy in a reasonable computational time compared with classical method, variance estimation method and facial extraction method.

An efficient scheme for face detection based on contours and feature skin recognition

Human face detection is very useful in many applications such as communications, automatic access control systems, video browsing, security control, verification of credit cards, identifying criminals and so on. This work provides a simple and efficient technique to detect human faces in still images. The new method based on skin color, contour drawing and feature extraction. The features under consideration are eyes, nose and mouth. The technique used to extract facial features developed based on feature location with respect to face dimensions. The proposed algorithm was tested on various images and its performance was found to be good in most cases. Experimental results show that our method of human face detection achieves very encouraging results with good accuracy, great speed and simple computations.