@MastersThesis{Thaller2016,
  author   = {Thaller, Hannes},
  school   = {Johannes Kepler University},
  title    = {Towards Deep Learning Driven Design Pattern Detection},
  year     = {2016},
  abstract = {Design patterns are elegant and well tested solutions to recurrent
	software development problems. Their extensive use in every day programming
	weaves valuable architectural information into software systems.
	Despite the wide usage of design patterns, system documentations
	seldom contain information about their existence. This work presents
	a fully fledged approach to extract design patterns such that the
	lost information can be of value for architects, developers and maintainers.
	It includes the common design pattern detection steps that extract
	features, sample candidates from the system under inspection and
	infer whether the candidates are of a certain pattern or not. The
	approach incorporates the usage of object oriented properties in
	form of micro-structures that are projected onto feature maps. These
	feature maps are then analyzed by a convolutional neural network
	that extracts high-level features from which robust prediction results
	can be drawn. Results indicate that deep learning methods bare great
	potential for the design pattern community as reliable inference
	procedure.},
  file     = {:MSc Theses\\2016 Hannes Thaller\\Towards Deep Learning Driven Design Pattern Detection-preprint.pdf:PDF},
  keywords = {SCCH},
}