Quantitative approaches in object-oriented software engineering
Author(s)
Bibliographic Information
Quantitative approaches in object-oriented software engineering
（Innovative technology series information systems and networks）
Hermes Penton Science, c2002
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
C||Quantitative-202017755
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Note
Derived from L'Objet, Quantitative Approaches in Objet-oriented Software Engineering, Vol.7, no.4
Description and Table of Contents
Description
Software internal attributes have been extensively used to help software managers, customers and users to characterize, assess and improve the quality of software products. Software measures have been adopted to increase understanding of how software internal attributes affect overall software quality. Estimation models based on software measures have been used successfully to perform risk analysis and to assess software maintainability, reusability and reliability. The object-oriented approach presents an advance in technology, providing more powerful design mechanisms and new technologies. These include OO frameworks, analysis/design patterns, architectures and components that have all been proposed to improve software engineering productivity and software quality. The key topics in this publication cover metrics collection, quality assessment, metrics validation and process management. The contributors are from leading research establishments in Europe, South America and Canada.
Table of Contents
1. A formal approach to building a polymorphism metric. Claudia Pons and Luis Olsina 2. A merit factor-driven approach to a modularization of object-oriented systems. Fernando Brito e Abreu and Miguel Goulao. 3. Object-relational database metrics. Mario Piattini, Coral Calero, Houari Sahraoui and Hakim Lounis 4. Measuring event-based object-oriented conceptual models. Geert Poels and Guido Dedene 5. Class cohesion as predictor of changeability: an empirical study. Hind Kabaili, Rudolf Keller and Francois Lustman 6. Buidling quality estimation models with fuzzy threshold values. Houari A Sahraoui, Mounir Boukadoum and Hakim Lounis
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