References of Recognizer

@Article{	  rich.wills:recognizing,
  author	= {Charles Rich and Linda M. Wills},
  title		= {Recognizing a Program's Design: A Graph-Parsing Approach},
  journal	= {IEEE Software},
  year		= {1990},
  volume	= {7},
  number	= {1},
  pages		= {82-89},
  month		= jan,
  inhalt	= {Ein prototypisch implementierter Recognizer erkennt
		  sogenannte Cliches im Programmcode. Cliches sind häufig
		  verwandte Programmiermuster wie binäre Suche oder
		  Listenverarbeitung. Die Cliches sind als Plangraphen in
		  einer Datenbasis abgelegt. Der Programmcode wird
		  gleichfalls anhand des Kontroll- und Datenflusses in einen
		  Plangraphen umgewandelt. Der Recognizer versucht
		  Plangraphen der Datenbasis mit dem Plangraphen des
		  Programmcodes zur Deckung zu bringen, um so Cliches zu
		  entdecken. Bei gefundener Deckung wird das Programmstück
		  natürlichsprachlich (mittels Schablonen) beschrieben.},
  note		= {In this paper it is assumed that most programmers use
		  similar structures to program. Such so-called cliches can
		  be recognized automatically and can then be used to
		  generate the documentation of the program},
  class		= {Software_Reverse_Engineering, Reverse_Design,
		  Knowledge-Based_Concept_Assignment,
		  Program_Plan_Assignment_by_Parsing, Recognizer}
}

@InProceedings{	  wills:flexible*1,
  author	= {Linda M. Wills},
  title		= {Flexible Control for Program Recognition},
  booktitle	= {Working Conference on Reverse Engineering},
  address	= {Baltimore, Maryland},
  year		= {1993},
  month		= may,
  pages		= {134-143},
  abstract	= {Recognizing commonly used data structures and algorithms
		  is a key activity in reverse engineering. Systems developed
		  to automate this recognition process have been isolated,
		  stand-alone systems, usually targeting a specific task. We
		  are interested in applying recognition to multiple tasks
		  requiring reverse engineering, such as inspecting,
		  maintaining, and reusing software. This requires a
		  flexible, adaptable recognition architecture, since the
		  tasks vary in the amount and accuracy of knowledge
		  available about the program, the requirements on
		  recongnition power, and the resources available. We have
		  developed a recognition system based on graph parsing. It
		  has a flexible, adaptable control structure that can accept
		  advice from external agents. Its flexibility arises from
		  using a chart parsing algorithm. We are studying this graph
		  parsing approach to determine what types of advice can
		  enhance its capabilities, performance, and scalability.},
  ftp		= {ftp.cc.gatech.edu//pub/groups/reverse/repository/flexible.ps}
		  ,
  class		= {Software_Reverse_Engineering, Reverse_Design,
		  Knowledge-Based_Concept_Assignment,
		  Program_Plan_Assignment_by_Parsing, Recognizer}
}

@InProceedings{	  wills:using,
  author	= {Linda M. Wills},
  title		= {Using Attributed Flow Graph Parsing to Recognize
		  Programs},
  booktitle	= {Int. Workshop on Graph Grammars and Their Application to
		  Computer Science},
  address	= {Williamsburg, Virginia},
  year		= {1994},
  month		= nov,
  ftp		= {ftp.cc.gatech.edu//pub/groups/reverse/repository/ggram.ps}
		  ,
  class		= {Software_Reverse_Engineering, Reverse_Design,
		  Knowledge-Based_Concept_Assignment,
		  Program_Plan_Assignment_by_Parsing, Recognizer}
}