Advanced Methods for Software Process Support

Abstract

Effort overruns is common problem in software development. This dissertation thesis is focused on design of new advanced method for software process support in early phase of software development. In particular, this method helps to improve software development process using results of classification of software requirements. Those requirements are experimentally classified using machine-learning methods like neural network or Naïve Bayes classifier. Results of classification help to project managers or analysts make estimations of time duration of work more accurately. Part of this PhD thesis provides a guideline for software effort estimation. Companies should be able to deploy, configure and use proposed methodology using the guideline. An estimation process should be also improving continuously.