Project Name: Easy Win Win Keyword Analyzer

Sponsor:
Hasan Kitapci (email: kitapci@cse.usc.edu)

Partners:
Dr. Barry Boehm (email: boehm@cse.usc.edu)

Background:
EasyWinWin is a groupware-supported methodology for requirements negotiation that builds on the win-win negotiation approach and leverages collaborative technology to improve the involvement and interaction of key stakeholders. With EasyWinWin, stakeholders move through a step-by-step win-win negotiation where they collect, elaborate, and prioritize their requirements, and then surface and resolve issues. However, during the negotiation some analysis will be automatically done to improve the negotiation results. This project focus on investigating how to use some techniques, such as Keyword Analysis, in order to help; the stakeholders to review the results during the negotiations to find out the project keywords, win conditions and pre-requirements, and the researchers to analyze the data and improve the requirements negotiation process.

Problem:
The goal of this project is to investigate some techniques to improve the results of EasyWinWin requirements negotiation steps. Extracting some of the important phrases out of the negotiated outputs will help the stakeholders to have better results and understanding about the project. The extracted data will also be used to create requirements of the project.

Desired deliverables of the project are list of project keywords, the noun-phrases and verb-phrases that will be helpful for win-win artifacts and requirements specification, contradictions between words used to describe the same object, etc.

Converting the data into an XML format and using some text analysis techniques or COTS to analyze the data for different EasyWinWin negotiation steps such as brainstorming stakeholder interests to improve the process outputs.

Constraints:
None specified

Desired Deliverables:
There are some tools that can be evaluated in order to use in this project. One of these tools is Co-word analysis, which is a content analysis technique that is effective in mapping the strength of association between keywords in textual data. Investigate tools to do some information extraction, natural language processing, and text analysis.