@article {814,
	title = {Machine Learning in Predicting the Appropriate Model of Software Process Models Deviation-},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {12/.2018},
	pages = {308-323},
	type = {Research},
	chapter = {308},
	abstract = {<p>\&nbsp;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\&nbsp; the process model execution. Journal of Digital Information Management {\textquotedblleft}priority and dependency of sub-activities {\textquotedblleft} and data from previous executions to predict the appropriate software process model. Subject Categories and Descriptors D.2.3 [Software Coding Tools and Techniques]; F.1.1 Models of Computation 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</p>
},
	doi = {https://doi.org/10.6025/jdim/2018/6/289-307},
	url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_3.pdf},
	author = {Tarik Chaghrouchni and Issam Mohammed Kabbaj and Zohra Bakkoury}
}
@article {703,
	title = {Machine Learning in Predicting the Appropriate Model of Software Process Models Deviation},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {12/2018},
	pages = {308-323},
	type = {Research},
	chapter = {308},
	doi = {https://doi.org/10.6025/jdim/2018/6/308-323},
	url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_3.pdf},
	author = {Tarik Chaghrouchni and Issam Mohammed Kabbaj and Zohra Bakkoury}
}
