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Researchers have proposed several software reliability growth models, many of which possess complex parametric forms. In practice, software reliability growth models should exhibit a balance between predictive accuracy and other statistical measures of goodness of fit, yet past studies have not always performed such balanced assessment. This paper proposes a framework for software reliability growth models possessing a bathtub-shaped fault detection rate and derives stable and efficient expectation conditional maximization algorithms to enable the fitting of these models. The stages of the bathtub are interpreted in the context of the software testing process. The illustrations compare multiple bathtub-shaped and reduced model forms, including classical models with respect to predictive and information theoretic measures. The results indicate that software reliability growth models possessing a bathtub-shaped fault detection rate outperformed classical models on both types of measures. The proposed framework and models may therefore be a practical compromise between model complexity and predictive accuracy.
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Geosilhouettes: Geographical measures of cluster fit
Regionalization, under various guises and descriptions, is a longstanding and pervasive interest of urban studies. With an increasingly large number of studies on urban place detection in language, behavior, pricing, and demography, recent critiques of longstanding regional science perspectives on place detection have focused on the arbitrariness and non-geographical nature of measures of best fit. In this paper, we develop new explicitly geographical measures of cluster fit. These hybrid spatial–social measures, called geosilhouettes, are demonstrated to capture the “core” of geographical clusters in racial data on census blocks in Brooklyn neighborhoods. These new geosilhouettes are also useful in a variety of boundary analysis and outlier detection problems. In this paper, the thinking behind geosilhouettes is presented, their mathematical form is defined, they are demonstrated, and new directions of research are discussed.
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- Award ID(s):
- 1762160
- PAR ID:
- 10547012
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- Volume:
- 48
- Issue:
- 3
- ISSN:
- 2399-8083
- Format(s):
- Medium: X Size: p. 521-539
- Size(s):
- p. 521-539
- Sponsoring Org:
- National Science Foundation
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