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Using architecture analysis to evolve complex industrial systems
2008 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 5135, Springer, 2008, 326-341 p.Chapter in book (Refereed)
Abstract [en]

ABB is a large industrial company with a broad product portfolio that contains products that can be categorized as highly complex industrial systems. Software embedded in complex industrial systems must support rigid system dependability requirements. It is not only a challenge to design and implement these systems as dependable, but it is also difficult to maintain this important property over time. There are several factors that make software evolution a challenging task, such as: size of the software base is measured in order of MLOC, products are long-lived and extended to support new requirements over time longer than 10 years. Because of personnel turnover important knowledge is lost from time to time, and the only artifact that is really up-to-date is the implementation itself. Therefore, to obtain an up-to-date view of the system and prevent expensive mistakes during system evolution, it is beneficial to find practical ways to obtain an up-to-date view on an architectural level without having to read thousands of lines of source code. These activities should be seen as an important contribution for preventing the introduction of faults into software systems since they contribute to improve and maintain the overall system dependability. This experience paper provides practical advices on how to reconstruct the architecture of existing systems by combining the use of tools and the existing knowledge within the organization. The paper is based on experiences from two cases in different sub domains within industrial automation.

Place, publisher, year, edition, pages
Springer, 2008. 326-341 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5135
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-21166DOI: 10.1007/978-3-540-85571-2_14Scopus ID: 2-s2.0-51849089160ISBN: 354085570X (print)ISBN: 9783540855705 (print)OAI: oai:DiVA.org:mdh-21166DiVA: diva2:661772
Available from: 2013-11-04 Created: 2013-08-27 Last updated: 2016-05-17Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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