Issues on Estimating Software Metrics in a Large Software Operation

Software engineering metrics prediction has been a challenge for researchers throughout the years. Several approaches for deriving satisfactory predictive models from empirical data have been proposed, although none has been massively accepted due to the difficulty of building a generic solution applicable to a considerable number of different software projects. The most common strategy on estimating software metrics is the linear regression statistical technique, for its ease of use and availability in several statistical packages. Linear regression has numerous shortcomings though, which motivated the exploration of many techniques, such as data mining and other machine learning approaches. This paper reports different strategies on software metrics estimation, presenting a case study executed within a large worldwide IT company. Our contributions are the lessons learned during the preparation and execution of the experiments, in order to aid the state of the art on prediction models of software development projects.