Siamak Farshidi

Siamak Farshidi

I'm a senior researcher at University of Amsterdam (UvA). My research interests lie primarily in the area of knowledge engineering, conceptual modeling, and software architecture.



Please download my complete CV from here.
2016-2020
Ph.D. Computer Science
Utrecht University, Utrecht, The Netherlands
2012-2014
M.Sc. in Computer Software Engineering
Shiraz University, Shiraz, Iran
2008-2011
B.Sc. in Computer Software Engineeringg
ADIBAN higher education institute, Garmsar, Iran
2006-2008
Ad. in Computer Software
Technical college of Shahid Shamsipour, Tehran, Iran
2003-2005
Diploma in Computer Software
Technical and Vocational Training Institute of Shahid Karimi, Tehran, Iran
Farshidi, S. (2020). Multi-Criteria Decision-Making in Software Production (Doctoral dissertation, University Utrecht).[PDF]

Farshidi, S., Jansen, S., & Deldar, M. (2021). A decision model for programming language ecosystem selection: Seven industry case studies. Information and Software Technology , 106640. [PDF]

Farshidi, S., Jansen, S., & Fortuin, S. (2021). Model-driven development platform selection: four industry case studies. Software and Systems Modeling, 1-27. [PDF]

Farshidi, S., & Jansen, S. (2020, September). A Decision Support System for Pattern-Driven Software Architecture. In European Conference on Software Architecture (pp. 68-81). Springer, Cham. [PDF]

Farshidi, S., Jansen, S., & van der Werf, J. M. (2020). Capturing software architecture knowledge for pattern-driven design. Journal of Systems and Software, 169, 110714.[PDF]

Farshidi, S., Jansen, S., EspaƱa, S., & Verkleij, J. (2020). Decision support for blockchain platform selection: Three industry case studies. IEEE Transactions on Engineering Management, 67(4), 1109-1128. [PDF]

Farshidi, S., Jansen, S., De Jong, R., & Brinkkemper, S. (2018, July). A decision support system for cloud service provider selection problem in software producing organizations. In 2018 IEEE 20th Conference on Business Informatics (CBI) (Vol. 1, pp. 139-148). IEEE. [PDF]

Farshidi, S., Jansen, S., & Fortuin, S. (2020). Model-driven development platform selection: four industry case studies. Software and Systems Modeling, 1-27. [PDF]

Farshidi, S., Jansen, S., De Jong, R., & Brinkkemper, S. (2018). Multiple Criteria Decision Support in Requirements Negotiation. In REFSQ Workshops. [PDF]

Research Experience

AMUSE Project - Utrecht University

The AMUSE research project is an academic collaboration between Universiteit Utrecht and Vrije Universiteit Amsterdam to address software composition, configuration, deployment and monitoring challenges on heterogeneous cloud ecosystems through ontological enterprise modeling. The following sub-projects have been accomplished:

Designing and implementing a decision support system for supporting decision-makers with multi-criteria decision-making problems in software production.
Building a decision model for the database management system selection problem.
Building a decision model for the database management system selection problem.
Building a decision model for the cloud service provider selection problem.
Building a decision model for the blockchain platform selection problem.
Building a decision model for the programming language selection problem.
Building a decision model for the model-driven development platform selection problem.
Building a decision model for the software architecture pattern selection problem.
Hermeneutic Interpretation in Theory Development in Information Science.
The Role of Quality in Software Architecture: A Systematic Literature Review.

Heuristic Search in Global Optimization - Shiraz University

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. I was working as a master student under the supervision of Prof. Koorush Ziarati at Shiraz University. The following sub-projects have been accomplished:

Designing and implementing a Hybrid algorithm based on particle swarm optimization with two genetic operators for the multi-mode resource constraint scheduling problem.
Evaluating the efficiency of the meta-heuristic algorithms, such as Hill climbing, Simulated annealing, Genetic Algorithm, Artificial Ant Colony, and Particle Swarm optimization to solve the vehicle routing problem (VRP).