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has been cited by the following article:

• TITLE: Smart Development Process Enactment Based on Context Sensitive Sequence Prediction

  AUTHORS: Andreas Rausch, Michael Deynet

  KEYWORDS: Software Engineering; Software Process Description Languages; Software Processes; Process Enactment; Process Improvement; Machine Learning; Sequence Prediction

  JOURNAL NAME: Journal of Computer and Communications, Vol.1 No.5, December 12, 2013

  ABSTRACT: Actual software development processes define the different steps developers have to perform during a development project. Usually these development steps are not described independently from each other—a more or less formal flow of development step is an essential part of the development process definition. In practice, we observe that often the process definitions are hardly used and very seldom “lived”. One reason is that the predefined general process flow does not reflect the specific constraints of the individual project. For that reasons we claim to get rid of the process flow definition as part of the development process. Instead we describe in this paper an approach to smartly assist developers in software process execution. The approach observes the developer’s actions and predicts his next development step based on the project process history. Therefore we apply machine learning resp. sequence learning approaches based on a general rule based process model and its semantics. Finally we show two evaluations of the presented approach: The data of the first is derived from a synthetic scenario. The second evaluation is based on real project data of an industrial enterprise.