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.