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Piecemeal legacy migrating with an architectural pattern language: a case study.

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Title: Piecemeal legacy migrating with an architectural pattern language: a case study.
Authors: Goedicke, M.1, Zdun, U.1 uzdun@cs.uni-essen.de
Source: Journal of Software Maintenance & Evolution: Research & Practice. Jan2002, Vol. 14 Issue 1, p1-30. 30p. 9 Diagrams, 1 Chart.
Subjects: Computer systems, Systems migration, C (Computer program language), Computer architecture
Abstract: Numerous large applications that have evolved over many years are well-functioning and reliable, but have severe problems regarding flexibility and reuse. Due to the many fixes that were applied in a system's lifetime, it is often hard to customize, change or exchange system parts. Therefore, it is problematic to migrate such systems to a more flexible architecture or to new technologies. The document archive/retrieval system, discussed in this article, is an example of a large C system that had such problems. As a solution, we will sketch an architectural pattern language that involves patterns well-suited for a piecemeal migration process. The patterns aim at building and composing highly flexible black-box component architectures with an object-oriented glueing layer. We present a re-engineering case study for the document archive/retrieval system based on these patterns. The patterns are used to wrap the existing C implementations and integrate them with an object system. Moreover, the patterns introduce flexibility hooks into the hot spots of the architecture and let components define their required environment. This enables an easier future evolution of the system. The case study demonstrates a pattern language as an approach for piecemeal legacy migration apart from implementation details. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Software Maintenance & Evolution: Research & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Numerous large applications that have evolved over many years are well-functioning and reliable, but have severe problems regarding flexibility and reuse. Due to the many fixes that were applied in a system's lifetime, it is often hard to customize, change or exchange system parts. Therefore, it is problematic to migrate such systems to a more flexible architecture or to new technologies. The document archive/retrieval system, discussed in this article, is an example of a large C system that had such problems. As a solution, we will sketch an architectural pattern language that involves patterns well-suited for a piecemeal migration process. The patterns aim at building and composing highly flexible black-box component architectures with an object-oriented glueing layer. We present a re-engineering case study for the document archive/retrieval system based on these patterns. The patterns are used to wrap the existing C implementations and integrate them with an object system. Moreover, the patterns introduce flexibility hooks into the hot spots of the architecture and let components define their required environment. This enables an easier future evolution of the system. The case study demonstrates a pattern language as an approach for piecemeal legacy migration apart from implementation details. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Software Maintenance & Evolution: Research & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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