A Strategy for Comparing Alternative Software Development Life Cycle Models
IEEE Transactions on Software Engineering
A Formal Model for Software Project Management
IEEE Transactions on Software Engineering
C++, neural networks and fuzzy logic (2nd ed.)
C++, neural networks and fuzzy logic (2nd ed.)
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain
Software engineering (6th ed.)
Software engineering (6th ed.)
Encyclopedia of Software Engineering
Encyclopedia of Software Engineering
Rapid Development: Taming Wild Software Schedules
Rapid Development: Taming Wild Software Schedules
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Guide to the Software Engineering Body of Knowledge - SWEBOK
Guide to the Software Engineering Body of Knowledge - SWEBOK
Agile and Iterative Development: A Manager's Guide
Agile and Iterative Development: A Manager's Guide
Applying a Composite Process Framework (CPF) in Real Life Software Development Project
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Schaum's Outline of Software Engineering
Schaum's Outline of Software Engineering
The Certified Software Quality Engineer Handbook
The Certified Software Quality Engineer Handbook
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Selection of appropriate Software Development Life Cycles can increase projects success. Depending on the selected SDLC Software Development Life Cycle, one can decrease development time/cost, minimize overhead and risk exposure, manage uncertainty, improve quality, promote client relations, and provide better project tracking and control. Despite the benefits of using suitable SDLCs, it is generally difficult to select the most appropriate one. There is not enough information in the literature about the criteria and how to take them into account to select appropriate SDLCs. Frequently used SDLCs and their comparative properties were elicited from literature, Internet and experts according to the criteria to select appropriate SDLCs. A Fuzzy Logic FL system was developed according to the elicited knowledge about the criteria and their affect on selecting suitable SDLCs in various cases. Fuzzy input/output variables, membership functions and fuzzy rules were defined and generated according to the elicited knowledge. The FL system was tested for various inputs and improved according to the test results.