TESS: Automated Support for the Evolution of Persistent
Types
Barbara Staudt Lerner
Computer Science Department
University of Massachusetts, Amherst
lerner@cs.umass.edu
Abstract
Persistent data often has a long lifetime. During its lifetime, the
types that are used to structure the data may undergo evolution to
support new requirements or provide more efficient services. This
evolution often makes the persistent data inaccessible unless it also
evolves with the types. Existing systems that support type and data
evolution focus on changes isolated to individual types, thereby
limiting what can be easily accomplished during maintenance.
We extend this work by presenting a model of
compound type changes that can also describe changes simultaneously
involving multiple types
and their effects on data. We then describe Tess, a system to
automate the evolution of types and their associated data when the
types undergo compound type changes.