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Journal of Information Science and Engineering, Vol. 24 No. 6, pp. 1603-1628 (November 2008)

Identifying Metrics for Commercial-Off-the-Shelf Software with Inductive Inference Based on Characteristic Vectors

Chongwon Lee, Byungjeong Lee, Jaewon Oh+ and Chisu Wu++
School of Computer Science
University of Seoul
Seoul 130-743, Korea
+School of Computer Science and Information Engineering
The Catholic University of Korea
Seoul 137-701, Korea
++School of Computer Science and Engineering
Seoul National University
Seoul 151-742, Korea

Nowadays, many users and organizations are interested in acquiring COTS (commercial- off-the-shelf) software products instead of building software systems themselves as acquisition reduces development costs. COTS products are usually provided in a packaged style without the source code but with many ready-to-use functions. To assure the proper level of quality, many organizations provide quality evaluation and certification services for COTS. Generally, their vendors are reluctant to disclose the source code. Thus, the major way of quality evaluation and certification requires dynamic behavior testing, essentially black-box testing. Since observing every aspect of external software behavior is almost impossible, it is crucial to designate an adequate range for quality evaluation such as an adequate number of quality checklists or product quality metrics for external behavior testing. Hence, to establish rules of selecting quality evaluation criteria in systematic ways, there have been attempts to analyze and utilize the past records of software evaluation based on artificial intelligence techniques. A Bayesian belief network (BBN) is one of the methods using an inductive inference based on prior experiences. In this paper, we represent software as characteristic vectors having dependency relationships with the external product quality metrics. BBN is then used to infer the metrics for new software products.

Keywords: COTS software, characteristic vector, metric, inductive inference, black-box testing

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Received May 21, 2007; revised August 20, 2007; accepted October 25, 2007.
Communicated by Sy-Yen Kuo.
* This study was supported by the Research Fund, 2007 of The Catholic University of Korea and supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No. R01-2006-000-11150-0). + Corresponding author.
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