Alaa F. Sheta

Prediction of software reliability: a comparison between regression and neural network non-parametric models

In this paper, neural networks have been proposed as an alternative technique to build software reliability growth models. A feedforward neural network was used to predict the number of faults initially resident in a program at the beginning of a test/debug process. To evaluate the predictive capability of the developed model, data sets from various projects were used. A comparison between regression parametric models and neural network models is provided.

Software effort estimation by tuning COOCMO model parameters using differential evolution

Accurate estimation of software projects costs represents a challenge for many government organizations such as the Department of Defenses (DOD) and NASA. Statistical models considerably used to assist in such a computation. There is still an urgent need on finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via tuning mathematical model parameters is urgently needed. In [1] two new model structures to estimate the effort required for software projects using Genetic Algorithms (GAs) were proposed as a modification to the famous Constructive Cost Model (COCOMO). In this paper, we follow up on our previous work and present Differential Evolution (DE) as an alternative technique to estimate the COCOMO model parameters. The performance of the developed models were tested on NASA software project dataset provided in [2]. The developed COCOMO-DE model was able to provide good estimation capabilities.

A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index

Obtaining accurate prediction of stock index sig-nificantly helps decision maker to take correct actions to develop a better economy. The inability to predict fluctuation of the stock market might cause serious profit loss. The challenge is that we always deal with dynamic market which is influenced by many factors. They include political, financial and reserve occasions. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria.

Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms

In this paper we provide our preliminary idea of using Genetic Algorithms (GAs) to solve the ad hoc Wireless Sensor Networks (WSNs) distance optimization problem. Our objective is to minimize the communication distance over a distributed sensor network. The proposed sensor network will be autonomously divided into set of k-clusters (k is unknown) to reduce the energy consumption for the overall network. On doing this, we use GAs to specify; the location of cluster-heads, the number of clusters and the cluster-mumbers which, if chosen, will minimize the communication distance over the distributed sensor network.

Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms

WSN has been directed from military applications to various civil applications. However, many applications are not ready for real world deployment. Most challenging issues are still unresolved. The main challenge facing the operation of WSN is saving energy to prolong the network lifetime. Clustering is an efficient technique used for managing energy consumption. However, clustering is an NP hard optimization problem that can’t be solved effectively by traditional methods. Computational Intelligence (CI) paradigms are suitable to adapt for WSN dynamic nature. This paper explores the advantages of CI techniques and how they may be used to solve varies problems associated to WSN. Finally, a short conclusion and future recommendation is being provided.

Modeling of a winding machine using genetic programming

We present a new method for modeling the dynamics of a winding process using genetic programming and compare it with traditional modeling approaches. Data sets collected from an actual industrial process were used throughout the experiments. Three models were developed to describe the dynamics of the winding process. Experimental results are presented and discussed.

Modelling hot rolling manufacturing process using soft computing techniques

Steel making industry is becoming more competitive due to the high demand. In order to protect the market share, automation of the manufacturing industrial process is vital and represents a challenge. Empirical mathematical modelling of the process was used to design mill equipment, ensure productivity and service quality. This modelling approach shows many problems associated to complexity and time consumption. Evolutionary computing techniques show significant modelling capabilities on handling complex non-linear systems modelling. In this research, symbolic regression modelling via genetic programming is used to develop relatively simple mathematical models for the hot rolling industrial non-linear process. Three models are proposed for the rolling force, torque and slab temperature. A set of simple mathematical functions which represents the dynamical relationship between the input and output of these models shall be presented. Moreover, the performance of the symbolic regression models is compared to the known empirical models for the hot rolling system. A comparison with experimental data collected from the Ere[gtilde]li Iron and Steel Factory in Turkey is conducted for the verification of the promising model performance. Genetic programming shows better performance results compared to other soft computing approaches, such as neural networks and fuzzy logic.

Software Effort Estimation Inspired by COCOMO and FP Models: A Fuzzy Logic Approach

Budgeting, bidding and planning of software project effort, time and cost are essential elements of any software development process. Massive size and complexity of now a day produced software systems cause a substantial risk for the development process. Inadequate and inefficient information about the size and complexity results in an ambiguous estimates that cause many losses. Project managers cannot adequately provide good estimate for both the effort and time needed. Thus, no clear release day to the market can be defined. This paper presents two new models for software effort estimation using fuzzy logic. One model is developed based on the famous COnstructive COst Model (COCOMO) and utilizes the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model utilize the Inputs, Outputs, Files, and User Inquiries to estimate the Function Point (FP). The proposed fuzzy models show better estimation capabilities compared to other reported models in the literature and better assist the project manager in computing the software required development effort. The validation results are carried out using Albrecht data set.

Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: a comparative study

Bidding for contracts depends mainly on estimated costs of a given project, which makes an accurate estimation of effort and time required very important with great impact on budget computation and project success. Inaccurate estimates are likely lead to one or all of the following negative outcomes: failure in making a profit, increased probability of incomplete project and delay of project delivery date. In this paper, we provide a comparison between models developed for software cost estimation using particle swarm optimisation (PSO) algorithm, fuzzy logic (FL), and well-known cost estimation models such as Halstead, Walston-Felix, Bailey-Basili and Doty models. The performance of the developed models is evaluated based on the mean magnitude of relative error (MMRE) for NASA software projects.

A New Software Reliability Growth Model: Genetic-Programming-Based Approach

A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalize. In this paper we propose the use of Genetic Programming (GP) as an eVolutionary computation approach to handle the software reliability modeling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalized Poisson, NHPP and Schneidewind reliability models.