The DOI matches its Crossref record, with a small difference in title or year; 157 of the 158 references it cites were found in a registry or an index.
Self-adaptive software: Landscape and research challenges
Abstract
Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system'sself(internal causes, e.g., failure) orcontext(external events, e.g., increasing requests from users). Such a system is required tomonitoritself and its context,detectsignificant changes,decidehow to react, andactto execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is,how,what,whenandwhere, towards providing a unified view of…
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Chen, Z., Li, J., Li, N., Jiao, W., & Kang, E. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3708998 · Cited by 3
Andrade, S. S., & Macêdo, R. J. D. A. (2015) · Journal of Software Engineering Research and Development · doi:10.1186/s40411-015-0016-z · Cited by 3
Towards autonomic multimodal interaction
Avouac, P.-A., Nigay, L., & Lalanda, P. (2011) · doi:10.1145/2034649.2034653 · Cited by 3
Toward a deeper use of feedback control in the design of critical computing system components
Maggio, M., & Leva, A. (2010) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2010.5717760 · Cited by 3
Towards self-protecting smart metering: investigating requirements for the MAPE loop
Salehie, M., Pasquale, L., Omoronyia, I., & Nuseibeh, B. (2012) · University of Limerick Institutional Repository (University of Limerick) · doi:10.34961/433 · Cited by 3
Self-citationHardware virtualization for rapid and secure CE product development and life cycle management
Agavanakis, K. N., Panagakis, G., Tatlas, N., & Koutroubinas, S. (2011) · International Conference on Consumer Electronics · doi:10.1109/icce.2011.5722902 · Cited by 3
Towards Energy-Proportional State-Machine Replication
Eibel, C., & Distler, T. (2015) · Adaptive and Reflective Middleware · doi:10.1145/2834965.2834969 · Cited by 3
Principled Eliciting and Evaluation of Trade-Offs When Designing Self-Adaptive Systems Architectures
Andrade, S., & Macêdo, R. J. D. A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802855-1.00008-3 · Cited by 3
Context-Aware Adaptive System for Intelligent Transport Management
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The role of autonomous aggregators in IoT multi-core systems
Nikolopoulos, B., Dimopoulos, A. C., Νικολαϊδου, Μ., Dimitrakopoulos, G. J., & Anagnostopoulos, D. (2017) · International Conference on the Internet of Things (IoT) · doi:10.1145/3131542.3131548 · Cited by 3
Eliciting Requirements for Improving Users’ Behavior Using Transparency
Kaiya, H., Yoshioka, N., Washizaki, H., Okubo, T., Hazeyama, A., Ogata, S., & Tanaka, T. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-7796-8_4 · Cited by 3
Survey on Formation Verification for Ensembling Collective Adaptive System
Johari, M. H., Jawaddi, S. N. A., & Ismail, A. B. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-8403-6_19 · Cited by 3
Metadata
- Authors
- Salehie, M., & Tahvildari, L.
- Affiliations
- University of Waterloo (CA)
- Venue
- ACM Transactions on Autonomous and Adaptive Systems
- Published
- 2009-05
- Volume
- 4
- Issue
- 2
- Pages
- 1-42
- ISSN
- 1556-4665 (ISSN-L)
- 1556-4703
- OpenAlex
- W1964137226
- Cited by
- 1,266
- References
- 158
- Citations by year
- 2026: 25 · 2025: 45 · 2024: 35 · 2023: 34 · 2022: 50 · 2021: 42 · 2020: 69 · 2019: 70 · 2018: 91 · 2017: 109 · 2016: 99 · 2015: 102 · 2014: 119 · 2013: 105 · 2012: 110
- FWCI
- 96.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Autonomous and Adaptive Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Advanced Software Engineering Methodologies
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- self-adaptive software, self-* properties, distributed applications, context-aware systems, self-monitoring, self-configuration, self-healing, self-optimization, self-protection, research landscape, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
