References of Extracting_Business_Rules

    A Research Project: Developing a Technique for Extracting Business Rules from Procedural Code, Phil Glasier
    @Article{	  glasier:research,
      author	= {Phil Glasier},
      title		= {A Research Project: Developing a Technique for Extracting
    		  Business Rules from Procedural Code},
      journal	= {Reverse Engineering Newsletter},
      pages		= {Rev-5},
      class		= {Software_Reverse_Engineering, Extracting_Business_Rules}
    }
    
    
    ROSADE: A Methodology for the Extraction of Business Rules, Aaron Hanks
    @Article{	  hanks:rosade,
      author	= {Aaron Hanks},
      title		= {ROSADE: A Methodology for the Extraction of Business
    		  Rules},
      journal	= {Reverse Engineering Newsletter},
      pages		= {Rev-5 -- Rev-6},
      class		= {Software_Reverse_Engineering, Extracting_Business_Rules}
    }
    
    
    Data dependency elicitation in database reverse engineering, Henrard, Jean and Hainaut, Jean-Luc
    Available as
    hypertext.
    @InProceedings{	  henrard.hainaut:data,
      author	= {Henrard, Jean and Hainaut, Jean-Luc},
      title		= {Data dependency elicitation in database reverse
    		  engineering},
      booktitle	= {Proc. of the 5th European Conference on Software
    		  Maintenance and Reengineering (CSMR 2001)},
      pages		= {11--19},
      year		= {2001},
      editor	= {P. Sousa and J. Ebert},
      publisher	= {IEEE Computer society},
      keywords	= {database reverse engineering, program understanding,
    		  program slicing, db-main},
      abstract	= {Database reverse engineering (DBRE) attempts to recover
    		  the technical and semantic specifications of the persistent
    		  data of information systems. Dependencies between records
    		  (data dependency) form a major class that need to be
    		  recovered. Since most of these dependencies are not
    		  supported by the DBMS, (foreign keys are the main
    		  exception, at least in modern relational DBMS), they have
    		  not be explicitly declared in the database schema. Careless
    		  reverse engineering will inevitably ignore them, leading to
    		  poor quality conceptual schema. Several information sources
    		  can contribute to the elicitation of these hidden
    		  dependencies. The program source code has long been
    		  considered the richest, but also the most complex, of them.
    		  In this paper, we analyze and compare, through their
    		  respective quality and cost, different program
    		  understanding techniques that can be used to elicit data
    		  dependencies.},
      url		= {http://www.fundp.ac.be/recherche/publications/fr/37327.html}
    		  ,
      class		= {Data_Reverse_Engineering Reverse_Engineering_Tools
    		  Extracting_Business_Rules }
    }
    
    
    Design Recovery of Legacy Database Applications based on Possibilistic Reasoning, Jens H. Jahnke and Melanie Heitbreder
    Available as
    hypertext.
    @InProceedings{	  jahnke.heitbreder:design,
      author	= {Jens H. Jahnke and Melanie Heitbreder},
      title		= {Design Recovery of Legacy Database Applications based on
    		  Possibilistic Reasoning},
      booktitle	= {Proceedings of 7th IEEE International Conference of Fuzzy
    		  Systems (FUZZ'98)},
      publisher	= {IEEE Computer Society},
      year		= {1998},
      month		= {May},
      url		= {http://www.uni-paderborn.de/cs/jahnke.html},
      abstract	= {Industrial database applications often evolve over three
    		  or more generations of developers, cover several hundred
    		  thousand lines of code and maintain a vast amount of data.
    		  A rapidly growing number of companies face the problem that
    		  they have to adapt or modernise such existing legacy
    		  database applications (LDA) in order to keep up with
    		  emerging requirements. The documentation of such LDAs is
    		  often obsolete as they have been developed over several
    		  generations of programmers. This paper presents an
    		  application of possibilistic reasoning to infer the
    		  semantic information that is necessary to recover the
    		  conceptual design of an LDA. A dedicated, graphical
    		  language (called Generic Fuzzy Reasoning Nets) is
    		  introduced to specify and customise the applied reverse
    		  engineering process. The actual reasoning process is
    		  performed by a nonmonotonic inference engine based on fuzzy
    		  petri nets which supports lazy execution of expensive
    		  analysis operations.},
      keywords	= {data reverse engineering, expert system, uncertain
    		  reasoning, legacy database},
      class		= {Extracting_Business_Rules Software_Reverse_Engineering
    		  Database_Migration Reverse_Design Re-Design
    		  Process_Models_for_Reverse_Design Alteration }
    }
    

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