FEMOSAA : Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54138658
- Type
- D - Journal article
- DOI
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10.1145/3204459
- Title of journal
- ACM Transactions on Software Engineering and Methodology
- Article number
- 5
- First page
- -
- Volume
- 27
- Issue
- 2
- ISSN
- 1049-331X
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to reveal that existing approaches for Self-Adaptive Software (SAS) drastically fail to optimise for various non-functional requirements in complex systems. Key difficulties lie in high complexity, number of features, dependencies, and forms of domain knowledge which were often ignored in existing approaches.
The paper develops the fundamentals for synergising design and runtime domain knowledge, and employs new industrial-scale models for dynamically optimizing complex SAS, applied to services and cloud. It informed a road-mapping agenda on Dynamic and Adaptive Search-Based Software Engineering (EPSRC Programme). I was invited to ICSE – the most prestigious Software Engineering conference.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -