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ASPLe: a methodology to develop self-adaptive software systems with reuse
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (AdaptWise)ORCID iD: 0000-0002-7555-7300
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.ORCID iD: 0000-0001-5471-551x
2017 (English)Report (Other academic)
Abstract [en]

Advances in computing technologies are pushing software systems and their operating environments to become more dynamic and complex. The growing complexity of software systems coupled with uncertainties induced by runtime variations leads to challenges in software analysis and design. Self-Adaptive Software Systems (SASS) have been proposed as a solution to address design time complexity and uncertainty by adapting software systems at runtime. A vast body of knowledge on engineering self-adaptive software systems has been established. However, to the best of our knowledge, no or little work has considered systematic reuse of this knowledge. To that end, this study contributes an Autonomic Software Product Lines engineering (ASPLe) methodology. The ASPLe is based on a multi-product lines strategy which leverages systematic reuse through separation of application and adaptation logic. It provides developers with repeatable process support to design and develop self-adaptive software systems with reuse across several application domains. The methodology is composed of three core processes, and each process is organized for requirements, design, implementation, and testing activities. To exemplify and demonstrate the use of the ASPLe methodology, three application domains are used as running examples throughout the report.

Place, publisher, year, edition, pages
Linnaeus University , 2017. , p. 116
Keywords [en]
Self-adaptation, Reuse, Methodology, Variability, Uncertainty, Design
National Category
Computer Sciences Software Engineering Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science; Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-71583OAI: oai:DiVA.org:lnu-71583DiVA, id: diva2:1191286
Note

Technical report - LNU-CS-AdaptWise-TR-2017NA01

Available from: 2018-03-17 Created: 2018-03-17 Last updated: 2018-06-05Bibliographically approved

Open Access in DiVA

ASPLe2017(5137 kB)24 downloads
File information
File name FULLTEXT01.pdfFile size 5137 kBChecksum SHA-512
edf44c3f95f2581f2180bec5cd3a48dcaf2fe4ebb4a091e0af02c5dc4097ee9536139c13683c001b2e9f692f9e1339721eda54a9e2fabc3b783979065dc38a9e
Type fulltextMimetype application/pdf

Authority records BETA

Abbas, NadeemAndersson, Jesper

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • 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
  • text
  • asciidoc
  • rtf