Computing similarity in a reuse library system

Ostertag, Eduardo; Hendler, James A.; Díaz, Rubén Prieto; Braun, Christine L.

doi:10.1145/131736.131739

Cited by 141 publications

(45 citation statements)

References 3 publications

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“…Al-based work includes [3,27], and some recent work by Henniger [18], which uses a knowledge-base and statistical information to retrieve reusable components, based on keyword search from texts describing the components. However, because the characterization of the component behavior is completely informal, the behavior is unpredictable.…”

Section: Ai Approachesmentioning

confidence: 99%

Software component search

Goguen

Nguyen

Meseguer

et al. 1996

Journal of Systems Integration

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Abstract. An important problem in software development is to make better use of software libraries by improving the search and retrieval process, that is, by making it easier to find the few components you may want among the many you do not want. This paper suggests some ideas to improve this process: (1) Associate an algebraic specification with each software component; these specifications should include complete syntactic information, but need have only partial semantic information. (2) User queries consist of syntactic declarations plus results for sample executions. (3) User queries may be posed in standard programming notation, which is then automatically translated into algebraic notation. (4) Search is organized as ranked multi-level filtering, where each level yields a ranked set of partial matches. (5) Early stages of filtering narrow the search space by using computationally simple procedures, such as checking that the number of types is adequate. (6) Middle levels may find partial signature matches. (7) Pre-computed catalogues (i.e., indexes) can speed up early and middle level filtering. (8) Semantic information is used in a final filter with term rewriting, but complete verification is not attempted. (9) The series of filters is implemented incrementally, so as to backtrack to lower ranked components in case of failure. This approach avoids the need for complex theorem proving, and does not require any knowledge of algebraic specification from the user. Moreover, it does not require either specifications or queries to be complete or even fully correct, because it yields partial matches ranked by how well they fit the query. The paper concludes with a description of some preliminary experiments and some suggestions for further experiments.

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Section: Ai Approachesmentioning

confidence: 99%

Software component search

Goguen

Nguyen

Meseguer

et al. 1996

Journal of Systems Integration

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“…The traditional approaches include keyword searching [8], faceted classification [10][13], signature matching [17] and behavioral matching [5]18]. Traditional approaches are not efficient, and suffer from lower recall and precision.…”

Section: Related Workmentioning

confidence: 99%

A Multiple Viewed Interrelated Ontology Model for Holistic Component Specification and Retrieval

Liu

Kennedy

2009

Advances in Software Engineering

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Abstract. Despite the success that Component-Based Development has achieved so far, component mismatch remains as a major hurdle for wider and smoother component reuse due to the lack of effective and automated approaches to component specification and retrieval. This paper presents a novel ontology-based approach to solve the above problem via holistic, semanticbased and adaptation-aware component specification and retrieval. The Multiple-Viewed and Interrelated Component Specification ontology model (MVICS) provides an ontology based architecture to specify components in a spectrum of perspectives. A semantic-based component retrieval method is then developed and the result of retrieval is presented to CBD engineers in a comprehensive component matching profile. Uniquely, the effect of possible component adaptation is included in the MVICS model and associated component specification and retrieval, which enables a more systematic and holistic view in component specification and selection.

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“…b) Faceted classification and retrieval The faceted classification [9] [10] approach attempts to classify the objects in the repository based on predefined taxonomies, e.g. subjects.…”

Section: Component Retrievalmentioning

confidence: 99%