Title:
Obtaining Architectural Descriptions from Legacy Systems: The Architectural Synthesis Process (ASP)

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dc.contributor.advisor Abowd, Gregory D.
dc.contributor.author Waters, Robert Lee en_US
dc.contributor.committeeMember Potts, Colin
dc.contributor.committeeMember McCracken, Mike
dc.contributor.committeeMember Kazman, Rick
dc.contributor.committeeMember Rugaber, Spencer
dc.contributor.department Computing en_US
dc.date.accessioned 2005-03-01T19:30:23Z
dc.date.available 2005-03-01T19:30:23Z
dc.date.issued 2004-10-29 en_US
dc.description.abstract A majority of software development today involves maintenance or evolution of legacy systems. Evolving these legacy systems, while maintaining good software design principles, is a significant challenge. Research has shown the benefits of using software architecture as an abstraction to analyze quality attributes of proposed designs. Unfortunately, for most legacy systems, a documented software architecture does not exist. Developing a good architectural description frequently requires extensive experience on the part of the developer trying to recover the legacy system's architecture. This work first describes a four-phase process that provides a framework within which architectural recovery activities can be automated. These phases consist of: extraction (obtaining a subset of information about the legacy system from a single source), classification (partitioning the information based upon its viewpoint), union (combining all the information in a particular viewpoint into a candidate view), and fusion (cross-checking all candidate views for consistency. The work then concentrates on the major problem facing automated architectural recovery---the concept assignment problem. To overcome this problem, a technique called semantic approximation is presented and validated via experimental results. Semantic approximation uses a combination of text data mining and a mathematical technique called concept analysis to build a lattice of similar concepts between higher-level domain information and low-level code concepts. The experimental data reveals that while semantic approximation does improve results over the more traditional lexical and topological approaches, it does not yet fully solve the concept assignment problem. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 1229496 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/4832
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Software architecture en_US
dc.subject Reverse engineering
dc.subject Recovery
dc.subject.lcsh Software architecture Evaluation en_US
dc.subject.lcsh Reverse engineering en_US
dc.subject.lcsh Data mining en_US
dc.title Obtaining Architectural Descriptions from Legacy Systems: The Architectural Synthesis Process (ASP) en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Abowd, Gregory D.
local.contributor.corporatename College of Computing
relation.isAdvisorOfPublication a9e4f620-85d6-4fb9-8851-8b0c3a0e66b4
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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