Automatic construction of SP problem-solving resource space

Abstract

The automation and adaptability of software systems to the dynamic environment and requirement variation is quite critical ability in cloud computing. This paper tends to organize vast stacks of problem solution resources for software processes into a structured resource space according to their topic words. The Resource Space model is well-developed by continuously adapting to its surroundings, expanding example group and refining model information. Resource topics are extracted with TDDF algorithm from document resources. Topic networks are established with topic connection strength. Then these topic networks are transformed into maximum spanning trees that are divided into different classification parts with pruning operation. This work may promotes automation of RS-based software service and development of novel software development in cloud computing environment. © 2009 Springer-Verlag.