@article{2638,
  author = {Tarik Chaghrouchni, Issam Mohammed Kabbaj, Zohra Bakkoury},
  title = {Machine Learning in Predicting the Appropriate Model of Software Process Models Deviation},
  journal = {Journal of Digital Information Management},
  year = {2018},
  volume = {16},
  number = {6},
  doi = {10.6025/jdim/2018/16/6/308-323},
  url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_3.pdf},
  abstract = {Software Process Model deviation
management allows to supervise process model execution
and its adaptation when an inconsisteny is detected. The
process model should be adjusted based on specified
rules in order to ensure the continuity of the process
model execution.
Some works attempted to handle process model deviation
by adjusting fragments and considering some rules to
ensure that all constraints are met. Other studies
considered only critical constraints in order to avoid cost/
schedule overrun: rules considering the criticality of the
activities and their order. However, it is still generating
uncertain executions with no mastery on the final result.
However, execution history can help to identify the best
choice and improve the control of the model. Machine
learning techniques will investigate this execution history
and predict the behavior of a future execution.
In this paper, we present an approach using machine learning techniques to combine the Micro-Analyze method
“priority and dependency of sub-activities “ and data from
previous executions to predict the appropriate software
process model.},
}