Formal Specification under Fuzziness

Judging the quality of any decision making procedure is a key problem whenever there is no possibility of developing a sequence of experiments allowing some kind of ratio relative to good results. It may he the case that we have only chances for a unique experiment or no similar experiences are available, but it may be also the case that no standard experiment allows the observation of such a good behavior. simply because Such good behavior can not be properly defined, in terms of standard crisp experiments. This situation is quite often associated to complex decision making problems, Then the only support we can find for our decision is the decision process itself, i.e., the consistency of the arguments leading to such a decision. Checking the quality of such a procedure becomes in this framework a key issue. Since specification is being quite often poorly defined, we Postulate that the design and formal specification of algorithms and processes require a fuzzy approach.

[1]  J. Michael Spivey,et al.  The Z notation - a reference manual , 1992, Prentice Hall International Series in Computer Science.

[2]  Taghi M. Khoshgoftaar,et al.  Computational Intelligence in Empirical Software Engineering , 2004 .

[3]  Richard S. Bird,et al.  Introduction to functional programming using haskeu , 1998 .

[4]  Stephen G. MacDonell,et al.  Fuzzy logic for software metric models throughout the development life-cycle , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[5]  Jim Woodcock,et al.  Using Z - specification, refinement, and proof , 1996, Prentice Hall international series in computer science.

[6]  Javier Montero,et al.  The Role of Fuzziness in Decision Making , 2007 .

[7]  J. Montero,et al.  A functional tool for fuzzy first order logic evaluation , 2006 .

[8]  Gerald Kotonya,et al.  Software Requirements Engineering , 1999 .

[9]  Edsger W. Dijkstra,et al.  A method of programming , 1988 .

[10]  Witold Pedrycz,et al.  Computational intelligence as an emerging paradigm of software engineering , 2002, SEKE '02.

[11]  John Hunt,et al.  Assertions in Java , 1998 .

[12]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .

[13]  Anthony Hall,et al.  Correctness by Construction: Developing a Commercial Secure System , 2002, IEEE Softw..

[14]  Lotfi A. Zadeh,et al.  From Computing with Numbers to Computing with Words - from Manipulation of Measurements to Manipulation of Perceptions , 2005, Logic, Thought and Action.

[15]  Carolyn B. Seaman,et al.  Qualitative Methods in Empirical Studies of Software Engineering , 1999, IEEE Trans. Software Eng..

[16]  Javier Montero,et al.  FUZZY SPECIFICATION IN SOFTWARE ENGINEERING , 2007 .

[17]  Anthony Hall,et al.  Realising the Benefits of Formal Methods , 2005, J. Univers. Comput. Sci..

[18]  Javier Montero,et al.  Fuzzy specification of algorithms , 2007 .

[19]  Bertrand Meyer,et al.  Eiffel: The Language , 1991 .

[20]  R. Jakobson.  On Language , 1990 .

[21]  Lotfi A. Zadeh.  From Computing with Numbers to Computing with Words - From Manipulation of Measurements to Manipulation of Perceptions , 2000, Intelligent Systems and Soft Computing.

[22]  Shari Lawrence Pfleeger,et al.  Preliminary Guidelines for Empirical Research in Software Engineering , 2002, IEEE Trans. Software Eng..

[23]  Ana Pradera,et al.  Double aggregation operators , 2004, Fuzzy Sets Syst..

[24]  C. A. R. Hoare,et al.  An axiomatic basis for computer programming , 1969, CACM.

[25]  Walter F. Tichy,et al.  Should Computer Scientists Experiment More? , 1998, Computer.

[26]  Javier Montero,et al.  Specification and Computing States in Fuzzy Algorithms , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[27]  Javier Montero,et al.  Crisp Acts, Fuzzy Decisions , 1998 .