Cognitive support in software reengineering based on generic fuzzy reasoning nets

Fuzzy Sets and Systems

Volumn 145, Issue 1, 2004, Pages 3-27

  Jahnke, Jens H ^a  

^a UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Computational intelligence; Generic fuzzy reasoning nets; Imperfect knowledge; Reengineering; Soft computing; Software reverse engineering

Indexed keywords

ARTIFICIAL INTELLIGENCE; ERROR ANALYSIS; FUZZY SETS; INFORMATION TECHNOLOGY; LARGE SCALE SYSTEMS; OBJECT ORIENTED PROGRAMMING; PROBLEM SOLVING; REENGINEERING;

GENERIC FUZZY REASONING NETS; IMPERFECT KNOWLEDGE; SOFT COMPUTING; SOFTWARE REVERSE ENGINEERING;

SOFTWARE ENGINEERING;

EID: 2642549117     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2003.10.003     Document Type: Article

References (39)

1. F. Abbattista, F. Lanubile, G. Visaggio, Recovering conceptual data models is human-intensive, in: Proc. 5th Internat. Conf. on Software Engineering and Knowledge Engineering, San Francisco, CA, USA, 1993, pp. 534-543.
2. M. Andersson, Extracting an entity relationship schema from a relational database through reverse engineering, in: Proc. 13th Internat. Conf. of the Entity Relationship Approach, Manchester, Vol. 881, Springer, Berlin, 1994, pp. 403-419.
3. L. Allison, C.S. Wallace, C.N. Yee, When is a string like a string? in: Internat. Symp. on Artificial Intelligence and Mathematics, Ft. Lauderdale, Florida, January 1990.
4. R. Balzer, Tolerating inconsistency (Software Development), in: Proc. 13th International Conference on Software Engineering, Austin, Texas, May 1991, pp. 158-165.
5. E.L.A. Baniassad, G.C. Murphy, Conceptual module querying for software reengineering, in: Proc. 1998 Internat. Conf. on Software Engineering, IEEE Computer Society Press/ACM Press, Silver Spring, MD/New York, 1998 pp. 64-73.
6. M. Chechik, S. Easterbrook (Eds.), Workshop on Living with Inconsistency, Co-located with ICSE 2001, Toronto, 2001.
7. E.J. Chikofsky, CASE and reengineering: from archeology to software perestroika, in: Proc. 12th Internat. Conf. on Software Engineering, 1990, p. 122.
8. Chikofsky E.J., James H. Cross II, Reverse engineering and design recovery. a taxonomy IEEE Software. 7(1):1990;13-17.
9. Corbi T.A. Program understanding. challenge for the 1990s IBM Systems J. 28(2):1989;294-306.
10. Driankov D., Doherty P. A nonmonotonic fuzzy logic. Zadeh L.A., Kacprzyk J. Fuzzy Logic for the Management of Uncertainty. 1992;171-190 Wiley, New York.
11. D. Dubois, J. Lang, H. Prade, Possibilistic Logic, Clarendon Press, Oxford, 1994, pp. 439-503.
12. Dubois D., Prade H. An introduction to possibilistic and fuzzy logics. Smets P., Mamdani E.H., Dubois D., Prade H. Non-Standard Logics for Automated Reasoning. 1988;287-326 Academic Press, London.
13. Hajek P. On logics of approximate reasoning. Lecture Notes in Comput. Sci. 808:1994;17-29.
14. Harandi M.T., Ning J.Q. Knowledge-based program analysis. IEEE Software. 7(1):1990;74-81.
15. J. Jahnke, Management of uncertainty and inconsistency in database reverse engineering processes, Ph.D. Thesis, Department of Mathematics and Computer Science, Universitaet Paderborn, Warburger Str. 100, 33098 Paderborn, Germany, 1999.
16. J. Jahnke, C. Ryan (Eds.), Proc. Intl. Workshop on Soft-Computing Applied to Software Engineering (SCASE), University of Twente, Enschede, NL, 2001.
17. Jahnke J., Wadsack J. Workshop 14. 3rd internat. workshop on intelligent software engineering (wise-3) Software Eng. Notes. 26:2001;43-44.
18. Konar A., Mandal A.K. Uncertainty management in expert systems using fuzzy petri nets. 8(1):1996;96-105.
19. N.N. Karnik, J.M. Mendel, Introduction to type-2 fuzzy logic systems, in: Proc. 7th Internat. Conf. on Fuzzy Systems FUZZY-IEEE'98, Anchorage, USA, 1998, pp. 915-920.
20. R.K. Keller, R. Schauer, S. Robitaille, P. Page, Pattern-based reverse-engineering of design components, in: Proc. 21st Internat. Conf. on Software Engineering, ACM Press, New York, May 1999, pp. 226-235.
21. Krämer C., Prechelt L. Design recovery by automated search for structural design patterns in object-oriented software. Wills L., Baxter I., Chikofsky E. Third Working Conf. on Reverse Engineering. 1996;208-215 IEEE Computer Society Press, Silver Spring, MD.
22. Levenshtein V.I. Binary codes capable of correcting deletions, insertions and reversals. Soviet Phys. Dokl. 6:1966;707-710.
23. Marie Vans A., Von Mayrhauser A., Somlo G. Program understanding behavior during corrective maintenance of large-scale software. Internat. J. Human-Comput. Studies. 51(1):1999;31-70.
24. von Mises R. Grundlagen der wahscheinlichkeitsrechnung. Math. Zeitung. 5:1919;1-97.
25. Murphy G.C., Notkin D. Reengineering with reflexion models. a case study Computer. 30(8):1997;29-36.
26. G.C. Murphy, D. Notkin, E.S.-C. Lan, An empirical study of static call graph extractors, in: Proc. 18th Internat. Conf. on Software Engineering, Berlin, Germany, March 1996, IEEE, pp. 90-98.
27. Paass G. Probabilistic logic. Smets P., Mamdani E.H., Dubois D., Prade H. Non-Standard Logics for Automated Reasoning. 1988;213-251 Academic Press, London.
28. Poole D. A logical framework for default reasoning. Artificial Intelligence. 36(1):1988;27-47.
29. Premerlani W.J., Blaha M.R. An approach for reverse engineering of relational databases. Comm. ACM. 37(5):1994;42-49.
30. Quilici A. A memory-based approach to recognizing programming plans. Comm. ACM. 37(5):1994;85-93.
31. Quilici A., Chin D.N. DECODE: a cooperative environment for reverse-engineering legacy software. Wills L.M., Newcomb P., Chikofsky E.J. Proc: Second Working Conf. on Reverse Engineering. 1995;IEEE Computer Society Press, Silver Spring, MD.
32. Quilici A., Woods S., Zhang Y. Program plan matching. experiments with a constraint-based approach Sci. Comput. Programming. 36(2-3):2000;285-302.
33. Shafer G. A Mathematical Theory of Evidence. 1976;Princeton University Press, Princeton, NJ.
34. O. Signore, M. Loffredo, M. Gregori, M. Cima, Reconstruction of er schema from database applications: a cognitive approach, in: Proc. 13th Internat. Conf. of ERA, Manchester, Springer, Berlin, 1994, pp. 387-402.
35. Smets P. Belief functions. Smets P., Mamdani E.H., Dubois D., Prade H. Non-Standard Logics for Automated Reasoning. 1988;253-286 Academic Press, London.
36. Voorbraak F. A computationally efficient approximation of dempster-shafer theory. Internat. J. Man-Machine Studies. 30(5):1989;525-536.
37. Wills L.M. Using attributed flow graph parsing to recognize clichés in programs. Cuny J.E., Ehrig H., Engels G., Rozenberg G. Proc. 5th Internat. Workshop on Graph Grammars and their Application to Computer Science. Lecture Notes in Computer Science. Vol. 1073:1996;170-184 Springer, Berlin.
38. Zadeh L. The concept of a linguistic variable and its application to approximate reasoning - I. Inform. Sci. 8:1975;199-249.
39. W.R. Zhang, Bipolar fuzzy sets, in: Proc. 7th Internat. Conf. on Fuzzy Systems FUZZ-IEEE'98, Anchorage, USA, 1998, pp. 835-840.