Increasingly competitive software industry, where multiple systems serve the same application domain and compete for customers, favors software with creative features. To promote software creativity, research has proposed multi-day workshops with experienced facilitators, and semi-automated tools to provide a limited support for creative thinking. Such approach is either time consuming and demands substantial involvement from analysts with creative abilities, or useful only for existing large-scale software with a rich issue tracking system. In this dissertation, we present different approaches leveraging advanced natural language processing and machine learning techniques to provide automated support for generating creative software requirements with minimal human intervention. A controlled experiment is conducted to assess the effectiveness of our automated framework compared to the traditional brainstorming technique. The results demonstrate our frame-work’s ability to generate creative features for a wide range of stakeholders and provoke innovative thinking among developers with various experience levels.