The ability to handle changes is a characteristic feature of successful software projects. The problem addressed in this paper is what should be done in project planning and iterative replanning so that the project can react effectively to changes. Thus the work presents research results in software engineering, as well as transfer of methods in knowledge engineering to software engineering, applying the AI planning technique to software process modeling and software project management. Our method is based on inter-project experience and evolution patterns. We propose a new classification of software projects, identifying and characterizing ten software process evolution patterns and link them to different project profile. Based on the evolution patterns, we discuss the planning support for process evolution and propose several methods that are new or significantly extend existing work, e.g. cost estimation of process changes, evolution pattern analysis, and a coarse process model for the initial planning and the iterative replanning process. The preliminary results have shown that the study of evolution patterns, based on inter-project experience, can provide valuable guidance in software process understanding and improvement.

This paper presents a case study of Norwegian banking software house where the objective is to adopt a categorization framework for managing evolution in software projects to identify project profiles and evolution patterns, and to suggest improvements to better support frequent evolutions.Based on an analysis of collected evolution data from an ongoing case study, we elaborate a QIP-inspired method and own techniques to evolve corresponding process models in our Process-centered Software Engineering Environment, called EPOS.The method describes also how to synthesize and reuse evolution experience from completed projects to improve planning and estimation in new similar projects.The collected data demonstrates that requirement changes which are detected in later development phases, are major causes for cost overruns in the studied organization.