  Market-driven software intensive product development companies have been more
and more experiencing the problem of feature expansion over time. Product
managers face the challenge of identifying and locating the high value features
in an application and weeding out the ones of low value from the next releases.
Currently, there are few methods and tools that deal with feature
identification and they address the problem only partially. Therefore, there is
an urgent need of methods and tools that would enable systematic feature
reduction to resolve issues resulting from feature creep. This paper presents
an approach and an associated tool to automate feature identification for web
applications. For empirical validation, a multiple case study was conducted
using three well known web applications: Youtube, Google and BBC. The results
indicate that there is a good potential for automating feature identification
in web applications.
