Software Cost Estimation Using Artificial Neural Networks
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Software Cost Estimation Using Artificial Neural Networks
Author: Wani Zahid Hussain
language: en
Publisher: Independent Author
Release Date: 2023-02-04
Softwarecost estimation is the forecasting of development effort and development time mandatory needed to develop a software project. It is considered to be the very primary step of software development process and at the same time considered to be the key task as accurate assessments of growth of the current project, its delivery exactness and its cost control can only be achieved once calculated estimation is accurate. And at broader perspective an accurate estimation of a currently developing software project will result in landing the organization in a better schedule of its futuristic software projects too. With due above reason, software effort estimation has received a considerable amount of attention of many researchers from past so many decades. In other words, Software cost estimation is the summation of predictions of both building effort and calendar time used to develop a software project. The building effort includes the summation of working hours and the total of workers included in the process of soft project development. Just from the inception of software project development, organizations of this nature came across to the problem of poor estimations of development effort and development time of software projects.
Recent Advances in Artificial Intelligence in Cost Estimation in Project Management
This book focuses on the practical application of AI tools and techniques in software project management, offering detailed theoretical explanations and practical examples of over 40 state-of-the-art machine learning and deep learning algorithms applied across each project phase, as well as in risk and resource management. Helping the business world estimate projects more accurately while saving costs and resources is crucial in today’s rapidly changing, fast-paced technological landscape. Moreover, it presents specific aspects of combined approaches through ensemble models, incorporating Taguchi’s optimization method to further improve estimation accuracy, advancing this area of software project management. A valuable resource for students and professionals to deepen their knowledge and skills, it also serves as a great manual for companies adopting smarter strategies to manage and develop projects more efficiently and effectively.
Artificial Neural Network Applications for Software Reliability Prediction
This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization. Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.