Machine Learning Approach For Cost And Effort Estimation In Agile Development Process PDF Download

Software project management is a key area in the field of computer science as software now-a-days impacts every area related to human life. Managing software means the process for development and the maintenance of software must be completely controlled using various pre-defined set of rules. As the software development process has to follow various parameters and a well-defined life cycle to ultimately deliver all the requirements gathered from the customers hence it has become quite time consuming and expensive process. It is also an evident fact that failure in software is caused mainly due to faulty practices used in project management. Using the right and optimised practices for software management helps both client as well as developers. Because of all the factors the need for highly reliable software is increasing. The reliability of software is mainly dependent on two factors: the selection of proper model for development and the estimation of various parameters. During the last few decades, the former area has been a research interest for many researchers resulting in development of many reliability models. Hence, currently parameter estimation is considered to be a primary activity in software reliability prediction and broadly the most important aspect of software project management. Software reliability models only become useful if they provide a correct and optimal estimation of various parameters. A successfully completed project means that the project is developed within the planned budget and timeline which is mostly related to accurate effort and cost estimation whereas inaccurate estimation of effort and cost results in failure of a project in context of delivery time, cost and other parameters. Hence the most important parameters requiring accurate estimate in terms of software projects are effort and cost. The accuracy of the estimation of these two vital parameters depends on the correct estimation of size of the project to be developed, and the ability to convert the size estimate into man hours, duration and cost.