References of Model_Generating

@Article{	  canfora.cimitile.ea:reverse,
  author	= {G. Canfora and Aniello Cimitile and De Carlini, Ugo},
  title		= {Reverse Engineering and Data Flow Diagrams in ADA
		  Environment},
  journal	= {Microprocessing and Microprogramming},
  year		= {1990},
  volume	= {30},
  pages		= {357-364},
  class		= {Software_Reverse_Engineering, Reverse_Specification,
		  Model_Generating}
}

@InProceedings{	  debaud:dare,
  author	= {Jean-Marc DeBaud},
  title		= {DARE: Domain-Augmented ReEngineering},
  booktitle	= {Proceedings of the Fourth Working Conference on Reverse
		  Engineering},
  publisher	= {IEEE Computer Society Press Los Alamitos California},
  year		= {1997},
  editor	= {Ira Baxter and Alex Quilici and Chris Verhoef},
  abstract	= {We present in this article the principles of a
		  domain-augmented reengineering approach (DARE) as well as
		  our initial experience applying sections of it. The
		  principal characteristic of the DARE approach is its focus
		  upon the computational context of a software system i.e.
		  the business or scientific domain to which it relates. This
		  context information is used both to drive the program
		  understanding as well as for the program evolution phases
		  of reengineering. In DARE a domain model (concepts and
		  associated relationships) serves as the structure denoting
		  context and is used for two purposes. First a dictionary of
		  possible domain concept realizations is populated. Second a
		  set of mappings from the domain to an existing tool or
		  library related to the domain is defined. Reengineering
		  then proceeds as follows: First a legacy system is analyzed
		  and annotated with the dictionary of domain concept
		  realizations. Then these matched concepts are transitioned
		  to the tool or library using the predefined mapping set.
		  Program evolution can then take place at the level of the
		  tool or library. Using our initial experience we discuss
		  DARE present an analysis and suggest implications for
		  future work.},
  class		= {Software_Evolution Software_Reverse_Engineering
		  Model_Generating Reverse_Specification Reverse_Design
		  Domain_Analysis Process_Models_for_Reverse_Design
		  Knowledge-Based_Concept_Assignmen }
}

@InProceedings{	  edwards.munro:abstracting,
  author	= {Helen M. Edwards and Malcolm Munro},
  title		= {Abstracting the Logical Processing Life Cycle for Entities
		  Using the RECAST method},
  pages		= {162--171},
  booktitle	= {Proceedings of the  International Conference on Software
		  Maintenance ~1993},
  year		= {1993},
  publisher	= {IEEE Computer Society Press},
  month		= sep,
  abstract	= {The Reverse Engineering into CASE Technology method
		  (RECAST) takes the source code for an existing COBOL system
		  and derives a no-loss representation of the system
		  documented in a Structured System Analysis and Design
		  Method (SSADM) format. One key element of the method is the
		  abstraction of logical processing that affects the
		  individual entities of the system. For each entity this
		  processing is extracted from the physical implementation of
		  the system using a program slicing technique and is then
		  transformed into a logical representation (as an Entity
		  Life History) using a set of translation and transformation
		  rules. This paper describes how the abstraction is achieved
		  and illustrates it with an example that was derived from an
		  existing operational system that has been used as a case
		  study for the method.},
  class		= {Software_Reverse_Engineering, Reverse_Specification,
		  Model_Generating}
}

@InProceedings{	  li.yang.ea:clarity,
  author	= {Yang Li and Hongji Yang and William Chu},
  title		= {Clarity Guided Belief Revision for Domain Knowledge
		  Recovery in Legacy Systems},
  booktitle	= {Proceedings of the 12th International Conference on
		  Software Engineering and Knowledge Engineering (SEKE2000)},
  publisher	= {Knowledge System Institute},
  year		= {2000},
  editor	= {Daniel E. Cooke and Joseph E. Urban},
  chapter	= {},
  pages		= {248-255},
  address	= {Chicago, USA},
  month		= {June},
  url		= {},
  abstract	= {Program understanding is the process of acquiring
		  knowledge from a computer program. Although research work
		  utilising knowledge engineering techniques has been
		  undertaken in this field, it is our observation that a
		  thorough application of AI methodology has not been
		  sufficiently explored. In this paper, we present a clarity
		  guided belief revision approach to domain knowledge
		  recovery in legacy software systems. Novel solutions are
		  given to three key AI issues in the context of domain
		  knowledge recovery from source code: knowledge
		  representation, where concrete semantic network is
		  separated from abstract semantic network to better
		  accommodate uncertainty reasoning and propagation;
		  uncertainty reasoning, which borrows ideas from
		  confirmation theory and recasts them in the context of
		  semantic network reasoning; heuristic search, which is
		  designed on the principle of programming psychology. Our
		  approach is light-weighted. It can be used stand-alone or
		  as a complement to traditional heavy-weighted domain
		  knowledge recovery methods. },
  keywords	= {program understanding, knowledge recovery, semantic
		  network, belief revision, heuristic search, programming
		  psychology},
  note		= {This paper describes our innovative work where
		  psychology-based methodology was brought into the area of
		  Articial Intelligence and was applied in the field of
		  domain knowledge recovery from source code.},
  class		= {Knowledge-Based_Concept_Assignment System_Modularizatio
		  Model_Generating Reverse_Specification Metrics
		  Reverse_Design Domain_Analysis
		  Metric-Based_Methods_in_Reverse_Design
		  Human_Oriented_Concept_Assignment_by_Informal_Reasoning
		  Software_Reverse_Engineering }
}

@Article{	  li.yang.ea:concept-oriented,
  author	= {Yang Li and Hongji Yang and William Chu},
  title		= {A Concept-Oriented Belief Revision Approach to Domain
		  Knowledge Recovery from Source Code},
  journal	= {Journal of Software Maintenance: Research and Practice},
  year		= {2000},
  volume	= {12},
  number	= {6},
  abstract	= {Domain knowledge is the soul of software systems. After
		  decades of software development, domain knowledge has
		  reached a certain degree of saturation. The recovery of
		  domain knowledge from source code is beneficial to many
		  software engineering activities, in particular, software
		  evolution. In the real world, the ambiguous appearance of
		  domain knowledge embedded in source code constitutes the
		  biggest barrier to recovering reliable domain knowledge. In
		  this paper, we introduce an innovative approach to
		  recovering domain knowledge with enhanced reliability from
		  source code. In particular, we divide domain knowledge into
		  inter-connected knowledge slices and match these knowledge
		  slices against the source code. Each knowledge slice has
		  its own authenticity evaluation function which takes the
		  belief of the evidences it needs as input and the
		  authenticity of the knowledge slice as output. Moreover,
		  the knowledge slices are arranged to exchange beliefs with
		  each other through inter-connections, i.e., concepts, so
		  that a better evaluation of the authenticity of these
		  knowledge slices can be obtained. The decision on
		  acknowledging recovered knowledge slices can therefore be
		  made more easily. Rooted in cognitive science and social
		  psychology, our approach is also widely applicable to other
		  knowledge recovery tasks. },
  keywords	= {domain knowledge recovery, uncertainty reasoning,
		  cooperative behaviour, semantic network},
  note		= {It is the first attempt of applying social psychology
		  theory to the field of knowledge recovery, in particular
		  design recovery.},
  class		= {Knowledge-Based_Concept_Assignment Using_graphs
		  Model_Generating Reverse_Specification
		  Cognitive_Processes_in_Human_Program_Understanding
		  Reverse_Design Domain_Analysis
		  Human_Oriented_Concept_Assignment_by_Informal_Reasonin
		  Intermediate_Representations_of_Source_Code
		  Software_Reverse_Engineering }
}

@InProceedings{	  li.yang.ea:towards,
  author	= {Yang Li and Hongji Yang and William Chu},
  title		= {Towards Building a Smarter Domain Knowledge Recovery
		  Assistant},
  booktitle	= {Proceedings of the 24th IEEE Annual Computer Software and
		  Applications Conference (COMPSAC2000)},
  publisher	= {IEEE Computer Society Press},
  year		= {2000},
  editor	= {},
  chapter	= {},
  pages		= {},
  address	= {},
  month		= {Oct},
  url		= {},
  abstract	= {Legacy systems need to be ``salvaged'' to prolong their
		  life circle. One way for such a salvation is to recover and
		  maintain domain knowledge embedded in legacy code. It is
		  our observation that existing methods or tools for domain
		  knowledge recovery from source code did not provide
		  maintainers with sufficient assistance to reduce the size
		  of analysable program sections, identify program sections
		  having intensive domain knowledge and maintain the belief
		  of a network of domain knowledge extracted from source code
		  which can accommodate change of belief coming from a user.
		  In this paper, we introduce techniques which can provide
		  software maintainers with smart assistance for the
		  above-mentioned three issues. },
  keywords	= {program partitioning, program readability metric, belief
		  network, domain knowledge recovery},
  note		= {We incorpate human psychology knowledge with the design of
		  a domain knowledge recovery tool.},
  class		= {Automated_Reverse_Design
		  Knowledge-Based_Concept_Assignment Reverse_Engineering_Tool
		  Model_Generating Reverse_Specification
		  Cognitive_Processes_in_Human_Program_Understanding Metrics
		  Reverse_Design System_Modularization Domain_Analysis
		  Recovery_of_Software_Architecture
		  Metric-Based_Methods_in_Reverse_Design
		  Human_Oriented_Concept_Assignment_by_Informal_Reasoning
		  Software_Reverse_Engineering }
}

@InProceedings{	  li.yang:fusing,
  author	= {Yang Li and Hongji Yang},
  title		= {Fusing Ambiguous Domain Knowledge Slices in a Reverse
		  Engineering Process},
  booktitle	= {Proceedings of the 7th Asia-Pacific Software Engineering
		  Conference (APSEC2000)},
  publisher	= {IEEE Computer Society Press},
  year		= {2000},
  editor	= {},
  chapter	= {},
  pages		= {},
  address	= {Singapore},
  month		= {Dec},
  url		= {},
  abstract	= {Recovering domain knowledge from legacy code plays an
		  important role in the new information technology era, which
		  can be of help for program understanding, system evolution
		  and software reuse. Traditional methods for domain
		  knowledge recovery from source code did not sufficiently
		  address the issue of ambiguity handling, in particular, the
		  propagation of ambiguity among multiple domain knowledge
		  slices recovered from source code in software reverse
		  engineering process. In this paper, we present a novel
		  approach to recovering unambiguous domain knowledge from
		  legacy code, where isolated ambiguous domain knowledge
		  slices are ``fused'' together in an iterative ambiguity
		  propagation process and hence the disambiguity of these
		  recovered knowledge slices is increased. },
  keywords	= {reverse engineering, domain knowledge recovery,
		  co-operative behaviour, belief revision},
  note		= {This is the first of this kind of work which deals with
		  the ambiguity involved in recovering large-scale domain
		  knowledge from source code.},
  class		= {Automated_Reverse_Design
		  Knowledge-Based_Concept_Assignment Using_graphs
		  Model_Generating Reverse_Specification
		  Cognitive_Processes_in_Human_Program_Understanding
		  Reverse_Design Domain_Analysis
		  Recovery_of_Software_Architectur
		  Metric-Based_Methods_in_Reverse_Design
		  Human_Oriented_Concept_Assignment_by_Informal_Reasoning
		  Intermediate_Representations_of_Source_Code
		  Software_Reverse_Engineering }
}