An empirical study on the specification and selection of components using fuzzy logic

  • Authors:

  • Kendra Cooper;João W. Cangussu;Rong Lin;Ganesan Sankaranarayanan;Ragouramane Soundararadjane;Eric Wong

  • Affiliations:

  • The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas

  • Venue:
  • CBSE'05 Proceedings of the 8th international conference on Component-Based Software Engineering
  • Year:
  • 2005

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Abstract

The rigorous specification of components is necessary to support their selection, adaptation, and integration in component-based software engineering techniques. The specification needs to include the functional and non-functional attributes. The non-functional part of the specification is particularly challenging, as these attributes are often described subjectively, such as Fast Performance or Low Memory. Here, we propose the use of infinite value logic, fuzzy logic, to formally specify components. A significant advantage of fuzzy logic is that it supports linguistic variables, or hedges (e.g., terms such as slow, fast, very fast, etc.), which are convenient for describing non-functional attributes. In this paper, a new systematic approach for the specification of components using fuzzy logic is presented. First, an empirical study is conducted to gather data on five components that provide data compression capabilities; each uses a different algorithm (Arithmetic Encoding, Huffman, Wavelet, Fractal, and Burrows-Wheeler Transform). Data on the response time performance, memory use, compression ratio, and root mean square error are collected by executing the components on a collection of 75 images with different file formats and sizes. The data are fuzzified and represented as membership functions. The fuzzy component specifications are ranked using a set of test queries. Fuzzy multi-criteria decision making algorithms are going to be investigated for the selection of components in the next phase of the work.