Computing similarity in a reuse library system: an AI-based approach

ABSTRACT

This paper presents an AI based library system for software reuse, called AIRS, that allows a developer to browse a software library in search of components that best meet some stated requirement. A component is described by a set of (feature, term) pairs. A feature represents a classification criterion, and is defined by a set of related terms. The system allows to represent packages (logical units that group a set of components) which are also described in terms of features. Candidate reuse components and packages are selected from the library based on the degree of similarity between their descriptions and a given target description. Similarity is quantified by a nonnegative magnitude (distance) proportional to the effort required to obtain the target given a candidate. Distances are computed by comparator functions based on the subsumption, closeness, and package relations. We present a formalization of the concepts on which the AIRS system is based. The functionality of a prototype implementation of the AIRS system is illustrated by application to two different software libraries: a set of Ada packages for data structure manipulation, and a set of C components for use in Command, Control, and Information Systems. Finally, we discuss some of the ideas we are currently exploring to automate the construction of AIRS classification libraries.

REFERENCES

• 1: V. R. Basili , H. D. Rombach, Tailoring the software process to project goals and environments, Proceedings of the 9th international conference on Software Engineering, p.345-357, March 1987, Monterey, California, United States
• 2: ~BAsmI, V. R., ANn ROMBACH, H. D. The TAME project: Towards improvement-oriented ~software environments. IEEE Trans. Softw. Eng. 14, 6 (June 1988), 758-773.
• 3: T. J. Biggerstaff , C. Richter, Reusability framework, assessment, and directions, Software reusability: vol. 1, concepts and models, ACM Press, New York, NY, 1989 [doi>10.1145/73103.73104]
• 4: Grady Booch, Software Component with ADA, Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, 1987
• 5: ~BRAUN, C. L., AND SALISBURY, h.B. Software reuse in command and control systems. Tech. ~Rep., Contel Technology Center, Chantfily, Va., 1990.
• 6: Eugene Charniak , Drew McDermott, Introduction to artificial intelligence, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1985
• 7: Eugene Charniak , James R. Meehan , Christopher K. Reisbeck , Drew V. McDermott, Artificial Intelligence Programming, Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 1987
• 8: P. T. Devanbu , R. J. Brachman , P. G. Selfridge , B. W. Ballard, LaSSIE—a knowledge-based software information system, Proceedings of the 12th international conference on Software engineering, p.249-261, March 26-30, 1990, Nice, France
• 9: D. W. Embley , S. N. Woodfield, A knowledge structure for reusing abstract data types, Proceedings of the 9th international conference on Software Engineering, p.360-368, March 1987, Monterey, California, United States
• 10: W. B. Frakes , B. A. Nejmeh, An information system for software reuse, Software reuse: emerging technology, IEEE Computer Society Press, Los Alamitos, CA, 1988
• 11: ~GAOUANO, R.A. Issues in reusability ada library tools. In Proceedings of the 6th EFISS ~Symposium (Atlanta, Ga., 1989)
• 12: ~HENDLER, J. A., WONG, Y. C., VINCIGUERRA, A., AND MOGILENSKY, J. AIRS: an AI-based Ada ~reuse tool. In Proceedings of the AIDA Conference (Oct. 1987).
• 13: ~F. Semantm networks. Comput. Math. Appl. 23, (Mar. 1992), 2 5.
• 14: Y. S. Maarek , D. M. Berry, The use of lexical affinities in requirements extraction, Proceedings of the 5th international workshop on Software specification and design, p.196-202, April 1989, Pittsburgh, Pennsylvania, United States
• 15: ~NASA Goddard Space Fhght Center. Software Engineering Laboratory (SEL) Database ~Organization and User's GuLde, Revision 1, Feb. 1990 SEL-89-101.
• 16: N. J. Nilsson, Principles of artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1980
• 17: ~OSTERTAG, E. J., AND HENDLER, J.A. Art AI-based reuse system. Tech. Rep. CS-TR-2197, ~UMIACS-TR-89-16, Univ of Maryland, Dept. of Computer Science, Feb. 1989.
• 18: Eduardo J. Ostertag , James A. Hendler, Computing similarity in a reuse library system: an AI-based approach, University of Maryland at College Park, College Park, MD, 1991
• 19: Ruben Prieto-Diaz, A software classification scheme (reusability, libraries, development), 1985
• 20: ~PRIETO-Diaz, R. Domain analysis for reusability. In Proceedings of the COMPSAC '87 ~(Tokyo, Oct. 1987), 23-29.
• 21: R. Prieto-Diaz, Classification of reusable modules, Software reusability: vol. 1, concepts and models, ACM Press, New York, NY, 1989 [doi>10.1145/73103.73107]
• 22: H. Dieter Rombach, MVP-L: a language for process modeling in-the-large, University of Maryland at College Park, College Park, MD, 1991
• 23: Paolo A. Straub , Eduardo J. Ostertag, Semantics of the extensible description formalism, University of Maryland at College Park, College Park, MD, 1990
• 24: ~STRAUB, P. A., AND OSTERTAG, E.J. EDF, A formahsm for describing and reusing software ~experience In Internatmnal Symposzum on Software Rehablhty Engineering (Austin, Tex., ~May 17 18, 1991), 106 113.
• 25: M. Wood , I. Sommerville, An information retrieval system for software components, ACM SIGIR Forum, v.22 n.3-4, p.11-28, Spring/Summer 1988 [doi>10.1145/54347.54349]

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: Software libraries

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Complexity measures
      D.2.m Miscellaneous
          Subjects: Reusable software**

  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.3 Software Management
          Subjects: Software selection

General Terms:
Design, Theory

Keywords:
facet classification, similarity-based retrieval