Abstract
Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.
Highlights
The key role of project management is to estimate an effort to develop software projects
The graph shows that 19% of the projects are estimated with an error less than 25%
The International Function Point Users Group (IFPUG) method is able to estimate more than 60% of projects with an error of less than 50% if the product delivery rate (PDR) based on Industry sector (IS) variables is used
Summary
The key role of project management is to estimate an effort to develop software projects. In this article, selected methods for estimating the development effort of software projects are addressed. Software development suffers from poor project management and poor budgeting. Development effort and budgeting should be correlated—symmetrical to software size. Software size is a value of software complexity. It is measured in various units; function points, use case points, lines of codes, etc. It is measured in various units; function points, use case points, lines of codes, etc. [1,2,3,4]
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