a novel software cost estimation techniques using fuzzy methodsДобро пожаловать в интернет магазин товаров для дома Artstroyvrn.ru. Мы рады предложить вам широкий ассортимент светильников, мебели, декора, текстиля и уникальных дизайнерских изделий по привлекательным ценам!
Momentarily software cost estimation has protrusive role in software engineering practice. The most desired capabilities of any software development organization are its cost estimation for software projects. It helps the customers to make successful investments and also assist software project manager in making sustainable decisions during project execution. In order to fulfill the requirements of cost estimation, time effective software is required. By keeping in view of all these requirements, my book introduces a novel models of interval type-1 and type-2 fuzzy logic estimation effort in software in software development. This book touches upon MATLAB which is tuning parameter for various cost estimation model. published software projects data model performance is used along with comparison of my novel model and existing ubiquitous models.
Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners’ acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy models and Fuzzy numbers in addition to Feature Subset Selection.
Accurately estimating software size, cost, effort, and schedule is probably the biggest challenge facing software developers today. Traditional intuitive estimation methods have consistently produced optimistic results which contribute to the too familiar cost overrun and schedule slippage. It is observed that different methods give different results for the same inputs because many intangible of historical data and every software project will have its own unique features and cannot be compared with future projects. Experts suggest using more than one estimation method and analyzing the results before making a decision. An accurate estimate of software size is an essential element in the calculation of estimated project costs and schedules. In this paper we have taken different projects and estimate their effort using various models. We also used fuzzy logic approach.We also done its implementation.
Software cost estimation is an important task for any software development organization. Its inaccurate estimates can lead to catastrophic results for both the developers and the customers. This book presents neural network techniques for software cost estimations.Here, the author has given three techniques using neural networks.The first one uses the Perceptron Neural Network, the second one uses Back Propagation Neural Network and the third one uses Functional Link Neural Network.Using all these techniques has led to improved software cost estimations.
Software engineering society has always faced the problems of accuracy of Software effort estimation. To advance the estimation accuracy of software effort, many studies have focused on effort estimation methods without any concern of data quality, although data quality is one of important factor to impact to the estimation accuracy. So I investigated the influence of outlier elimination upon the accuracy of software effort estimation through experiments applying two outlier elimination methods (K-means clustering and My-K-means clustering) and two effort estimation methods( Least squares and Neural network) associatively. A new outlier elimination method My-K-means clustering is proposed which gives better estimation results than K-means clustering. The experiments were performed using the Bank data set which consists of the project data performed in a bank in Pakistan, with or without outlier elimination.
Software cost estimation has been growing in importance till today. This is not a surprising fact that various difficulties are observed when estimating software costs. The major amount of the total costs of a project arises from the salaries of the personnel. Other costs, as license fees or new equipment for example, occur only once and are not too hard to estimate. Its a difficult task to measure software cost. These are various methods which were evolved time to time for cost estimation. A lots of examples of are given for cost estimation by different methods like Delphi, Analogy, COCOMO II etc.
Continuous changing scenarios of software development technology make effort estimation more challenging. Some of the difficulties of estimation arise from the complexity and invisibility of software. Software development is intensively human activity and can’t be free from error. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. The application of artificial neural networks in prediction of effort in conventional and Object Oriented Software development approach has been discussed.
At early stages of a construction project, the design information and scope definition are very limited, hence; during conceptual cost estimation, achieving high accuracy is very difficult. The level of uncertainty included in the cost estimations should be emphasized for making correct decisions. By using range estimating, the level of uncertainties can be identified in cost estimations. This study represents integrations of parametric and probabilistic cost estimation techniques in a comparative base. Combinations of regression analysis, neural networks, case–based reasoning and bootstrap method are proposed for the early range cost estimations of mass housing projects. The methods are applied using the data of mass housing projects financed by Housing Development Administration of Turkey (TOKI). Results of the three different approaches are compared for predictive accuracy and predictive variability, and suggestions for early range cost estimation of construction projects are made. This study should be especially useful to professionals of cost estimation and bid preparation fields who need practical methods to be applied in conceptual cost estimation processes.
Software cost estimation is the art of balancing time and resources to optimally budget projects. Most estimation models consist of mathematical algorithms or parametric relations and are used to approximate the dominant cost for developing software, the human effort. The models and techniques in this book are primarily based on the factors of People - Process - Product and through extensive experimentation with benchmark data obtained from the relevant literature they are proven viable and practical alternatives to traditional models. Moreover, they improve accuracy and increase comprehensibility over the risks occurring. Both quantitative and qualitative approaches are adopted. The quantitative approach, improves estimation accuracy, reliability and generalisability by exploring Computational Intelligence techniques, such as Artificial Neural Networks, Evolutionary Algorithms, Fuzzy Logic, and hybrid forms of these techniques. The qualitative approach extends the numerical and empirical Computational Intelligence investigations, by employing Fuzzy Cognitive Maps and Influence Diagrams, which facilitate in understanding the cause-and-effect dependencies of cost factors and effort.
In the current book there exists a practical guideline, which is most referenced by Mathwork (MatLab userguide software 2001 version), useful for economists and students of economics in order to apply Neural-Fuzzy approach in econometrics estimation in a simple way.
Software development efforts estimation is the process of predicting the most realistic use of effort required to develop or maintain the software project. The ability to accurately estimate the time and/or cost taken for a project to come in to its successful conclusion is a serious problem for software engineers. The use of a repeatable, clearly defined and well understood software development process has, in recent years, shown itself to be the most effective method of gaining useful historical data that can be used for statistical estimation. These estimates are ultimately influencing which features will be part of the project development. This book discusses the problems, issues and techniques of cost estimation and decision making.
A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods especially among adaboost varieties and particle filtering methods have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.
Software cost estimation is the process of predicting the effort required to develop a software system. Many estimation models have been proposed over the last 30 years. Models may be classified into 2 major categories: algorithmic and non-algorithmic. Each has its own strengths and weaknesses. A key factor in selecting a cost estimation model is the accuracy of its estimates. Unfortunately, despite the large body of experience with estimation models, the accuracy of these models is not satisfactory. The work includes comment on the performance of the estimation models and description of several newer approaches to cost estimation. A CBR based efficient search technique has been introduced so that can help to obtain the best result.
Manufacturing cost estimation usually takes place prior to the actual start of production, and as such is a forecast of manufacturing costs. The estimation is done based on software using information derived from the designers'' drawings or sketches. Since a major portion of the cost of a product is committed during the design phase, this work is intended to provide basic knowledge and tools to the designer to enable more cost-effective designs.
Cost estimation is a prediction process of defining the cost required in order to get the accurate cost of equipping facility, producing goods or providing services. It is important in managing project especially to the project manager when proposing budget for certain project. There are a few techniques that can be used to estimate software development such as Expert Judgment, Algorithmic Model, Price to Win, Estimation by Analogy, Parkinson Ian Estimation, Top-Down Estimation, Bottom-Up Estimation and Machine Learning. Nowadays, most web-based developers are still unaware of the different between cost estimation for web-based application and traditional application. As a result, the cost estimation for the web-based application or project becomes inaccurate. Furthermore, lot of projects included web-based application faced inaccurate estimation due to the poor and schedule estimation. Therefore, proper cost estimation for web-based application or project is needed. The survey result shows that cost estimation for web-based application is mostly done manually and produced inaccurate result.