Software project dynamics: an integrated approach
Software project dynamics: an integrated approach
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Using Process Simulation to Compare Scheduling Strategies for Software Projects
APSEC '02 Proceedings of the Ninth Asia-Pacific Software Engineering Conference
Scheduling Software Projects to Minimize the Development Time and Cost with a Given Staff
APSEC '01 Proceedings of the Eighth Asia-Pacific on Software Engineering Conference
Linking Software Process Modeling with Markov Decision Theory
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Computing Optimal Scheduling Policies for Software Projects
APSEC '04 Proceedings of the 11th Asia-Pacific Software Engineering Conference
An Overview of Software Cybernetics
STEP '03 Proceedings of the Eleventh Annual International Workshop on Software Technology and Engineering Practice
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In this paper, we highlight the application potential of process simulation techniques for software cybernetics research. Software engineering has seen many fruitful applications of simulation when modeling, understanding, and improving the software development process. In particular, process simulation has proven to be a valuable and efficient tool in our own software cybernetics research, having helped us to understand how scheduling policies actually behave in our discrete-time Markov decision process model for software projects. We outline how to advance the use of process simulation in our model to a much higher level: When computing optimal scheduling policies, simulation can be applied in the optimization step of the dynamic programming algorithms in order to save computation time. This approach resembles optimization techniques from the field of reinforcement learning, providing further evidence of the potential of simulation in software cybernetics.