Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods

The job of software effort estimation is a critical one in the early stages of the software development life cycle when the details of requirements are usually not clearly identified. Various optimization techniques help in improving the accuracy of effort estimation. The Support Vector Regression (SVR) is one of several different soft-computing techniques that help in getting optimal estimated values. The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained. The main objective of the research work carried out in this paper is to estimate the software effort using use case point approach. The use case point approach relies on the use case diagram to estimate the size and effort of software projects. Then, an attempt has been made to optimize the results obtained from use case point analysis using various SVR kernel methods to achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information Processing (IJIP

An estimate of necessary effort in the development of software projects

International Workshop on Intelligent Technologies for Software Engineering (WITSE'04). 19th IEEE International Conference on Automated Software Engineering (Linz, Austria, September 20th - 25th, 2004)The estimated of the effort in the development of software projects has already been studied in the field of software engineering. For this purpose different ways of measurement such as Unes of code and function points, generally addressed to relate software size with project cost (effort) have been used. In this work we are presenting a research project that deals with this field, us'mg machine learning techniques to predict the software project cost. Several public set of data are used. The analysed sets of data only relate the effort invested in the development of software projects and the size of the resultant code. For this reason, we can say that the data used are poor. Despite that, the results obtained are good, because they improve the ones obtained in previous analyses. In order to get results closer to reality we should find data sets of a bigger size that take into account more variables, thus offering more possibilities to obtain solutions in a more efficient way.Publicad

A systematic review of software development cost estimation studies

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods