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Unique developmental and operational characteristics of ML components as well as their inherent uncertainty demand robust engineering principles are used to ensure their quality.
R. Wirth and J. Hipp, “Crisp-dm: Towards a standard process model for data mining,” in International conference on the practical applications of knowledge discovery and data mining
2000
Earlier work this paper cites.
Addison-Wesley Professional, 2003
L. Bass, P. Clements, and R. Kazman, Software architecture in practice · 2003
Earlier work this paper cites.
M. Ciolkowski, O. Laitenberger, et al
2003
Earlier work this paper cites.
S. E. Hove and B. Anda, “Experiences from conducting semi-structured interviews in empirical software engineering research,” in International Software Metrics Symposium
2005
Earlier work this paper cites.
V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative research in psychology
2006
Earlier work this paper cites.
B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” 2007
2007
Earlier work this paper cites.
S. Easterbrook, J. Singer, M.-A. Storey, and D. Damian, “Selecting empirical methods for software engineering research,” in Guide to advanced empirical software engineering
2008
Earlier work this paper cites.
D. Budgen, M. Turner, P. Brereton, and B. A. Kitchenham, “Using mapping studies in software engineering.,” in PPIG
2008
Earlier work this paper cites.
B. A. Kitchenham and S. L. Pfleeger, “Personal opinion surveys,” in Guide to advanced empirical software engineering
2008
Earlier work this paper cites.
P. Kruchten, “What do software architects really do?,” Journal of Systems and Software
2008
Earlier work this paper cites.
N. B. Harrison and P. Avgeriou, “How do architecture patterns and tactics interact? a model and annotation,” Journal of Systems and Software
2010
Earlier work this paper cites.
ISO, “Systems and software engineering — systems and software quality requirements and evaluation,” tech. rep., Technical Report. ISO/IEC 25010, 2011
2011
Earlier work this paper cites.
D. S. Cruzes and T. Dyba, “Recommended steps for thematic synthesis in software engineering,” in International Symposium on Empirical Software Engineering and Measurement
2011
Earlier work this paper cites.
E. Begoli and J. Horey, “Design principles for effective knowledge discovery from big data,” in Joint Working Conference on Software Architecture and European Conference on Software Architecture
2012
Earlier work this paper cites.
B. A. Kitchenham, “Systematic review in software engineering: where we are and where we should be going,” in International Workshop on Evidential Assessment of Software Technologies
2012
Earlier work this paper cites.
N. Esfahani and S. Malek, “Uncertainty in self-adaptive software systems,” in Software Engineering for Self-Adaptive Systems II
2013
Earlier work this paper cites.
D. Sculley, G. Holt, D. Golovin, et al
2015
Earlier work this paper cites.
D. Maplesden, E. Tempero, J. Hosking, and J. C. Grundy, “Performance analysis for object-oriented software: A systematic mapping,” TSE
2015
Earlier work this paper cites.
L. A. Palinkas, S. M. Horwitz, C. A. Green, et al
2015
Earlier work this paper cites.
E. Woods, “Operational: The forgotten architectural view,” IEEE Software
2016
Earlier work this paper cites.
E. Woods, “Software architecture in a changing world,” IEEE Software
2016
Earlier work this paper cites.
M. Shahin, M. A. Babar, and L. Zhu, “Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices,” IEEE Access
2017
Earlier work this paper cites.
L. E. Li, E. Chen, J. Hermann, P. Zhang, and L. Wang, “Scaling machine learning as a service,” in International Conference on Predictive Applications and APIs
2017
Earlier work this paper cites.
S. Mahdavi-Hezavehi, P. Avgeriou, and D. Weyns, “A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements,” in Managing Trade-Offs in Adaptable Software Architectures
2017
Earlier work this paper cites.
B. Sena, L. Garcés, A. P. Allian, and E. Y. Nakagawa, “Investigating the applicability of architectural patterns in big data systems,” in Conference on Pattern Languages of Programs
2018
Earlier work this paper cites.
A. Aniculaesei, J. Grieser, A. Rausch, K. Rehfeldt, and T. Warnecke, “Toward a holistic software systems engineering approach for dependable autonomous systems,” in International Workshop on Software Engineering for AI in Autonomous Systems
2018
Cited alongside, same era.
Y. Nishi, S. Masuda, H. Ogawa, and K. Uetsuki, “A test architecture for machine learning product,” in International Conference on Software Testing, Verification and Validation Workshops
2018
Cited alongside, same era.
A. Batyuk, V. Voityshyn, and V. Verhun, “Software architecture design of the real-time processes monitoring platform,” in International Conference on Data Stream Mining & Processing
2018
Cited alongside, same era.
L. E. Lwakatare, A. Raj, J. Bosch, H. H. Olsson, and I. Crnkovic, “A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,” in International Conference on Agile Software Development
2019
Cited alongside, same era.
2020
Later among the works it cites.
A. Serban, K. van der Blom, H. Hoos, et al
2020
Later among the works it cites.
J. M. Zhang, M. Harman, L. Ma, and Y. Liu, “Machine learning testing: Survey, landscapes and horizons,” TSE
2020
Later among the works it cites.
H. Liu, S. Eksmo, J. Risberg, and R. Hebig, “Emerging and changing tasks in the development process for machine learning systems,” in International Conference on Software and System Processes
2020
Later among the works it cites.
D. Kaur, S. Uslu, and A. Durresi, “Requirements for trustworthy artificial intelligence–a review,” in International Conference on Network-Based Information Systems
2020
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Z. Wan, X. Xia, D. Lo, et al
2019
Cited alongside, same era.
X. Zhang, Y. Yang, Y. Feng, et al
2019
Cited alongside, same era.
F. Ishikawa and N. Yoshioka, “How do engineers perceive difficulties in engineering of machine-learning systems?,” in International Workshop on Conducting Empirical Studies in Industry
2019
Cited alongside, same era.
S. Amershi, A. Begel, C. Bird, et al
2019
Cited alongside, same era.
H. Washizaki, H. Uchida, F. Khomh, and Y.-G. Guéhéneuc, “Studying software engineering patterns for designing machine learning systems,” in International Workshop on Empirical Software Engineering in Practice (IWESEP)
2019
Cited alongside, same era.
H. Belani, M. Vukovic, and Ž. Car, “Requirements engineering challenges in building ai-based complex systems,” in International Requirements Engineering Conference Workshops
2019
Cited alongside, same era.
M. Chechik, “Uncertain requirements, assurance and machine learning,” in International Requirements Engineering Conference
2019
Cited alongside, same era.
E. de Souza Nascimento, I. Ahmed, E. Oliveira, M. P. Palheta, et al
2019
Cited alongside, same era.
Later among the works it cites.
A. Horneman, A. Mellinger, and I. Ozkaya, “AI engineering: 11 foundational practices,” tech. rep., Carnegie Mellon University, 2020
2020
Later among the works it cites.
M. Scheerer, J. Klamroth, R. Reussner, and B. Beckert, “Towards classes of architectural dependability assurance for machine-learning-based systems,” in International Symposium on Software Engineering for Adaptive and Self-Managing Systems
2020
Later among the works it cites.
L. Reimann and G. Kniesel-Wünsche, “Achieving guidance in applied machine learning through software engineering techniques,” in International Conference on Art, Science, and Engineering of Programming
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Kuwajima, H. Yasuoka, and T. Nakae, “Engineering problems in machine learning systems,” Machine Learning
2020
Later among the works it cites.
G. McGraw, H. Figueroa, V. Shepardson, and R. Bonett, “An architectural risk analysis of ml systems: Toward more secure ml,” Berryville Institute of Machine Learning, Clarke County, VA
2020
Later among the works it cites.
Adapting Software Architectures to Machine Learning Challenges - Supplementary Materials
2021
Closest in time.
[Online; accessed 12-08-2021]
H. Washizaki, H. Uchida, F. Khomh, and Y.-G. Guéhéneuc, “Machine learning architecture and design patterns.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
National Science and Technology Council (US). Select Committee on Artificial Intelligence, “The national artificial intelligence research and development strategic plan: 2019 update.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
Freeandopenmachinelearning, “ML reference architecture.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
M. Zinkevich, “Rules of machine learning: Best practices for ml engineering.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
G. Rios, “Patterns (and anti-patterns) for developing ML systems.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
D. Sato, A. Wider, and C. Windheuser, “Continuous delivery for machine learning.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
J. Everett, “Daisy architecture.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
P. Menon, “Demystifying data lake architecture.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
S. Chan, “A design pattern for machine learning with scala.” \url · 2021
Closest in time.
[Online; accessed 12-08-2021]
High-Level Expert Group on AI, “Ethics guidelines for trustworthy AI.” \url · 2021
Closest in time.
A. Serban, K. van der Blom, H. Hoos, and J. Visser, “Practices for engineering trustworthy machine learning applications,” IEEE 1st Workshop on AI Engineering
2021
Closest in time.