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ASPLe: a methodology to develop self-adaptive software systems with reuse
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV). (AdaptWise)ORCID-id: 0000-0002-7555-7300
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV).ORCID-id: 0000-0001-5471-551x
2017 (engelsk)Rapport (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linnaeus University , 2017. , s. 116
Emneord [en]
Self-adaptation, Reuse, Methodology, Variability, Uncertainty, Design
HSV kategori
Forskningsprogram
Data- och informationsvetenskap; Data- och informationsvetenskap, Datavetenskap; Datavetenskap, Programvaruteknik
Identifikatorer
URN: urn:nbn:se:lnu:diva-71583OAI: oai:DiVA.org:lnu-71583DiVA, id: diva2:1191286
Merknad

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

Tilgjengelig fra: 2018-03-17 Laget: 2018-03-17 Sist oppdatert: 2018-06-05bibliografisk kontrollert

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ASPLe2017(5137 kB)46 nedlastinger
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Abbas, NadeemAndersson, Jesper

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