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Due to the central role that conceptual data models play in the design of databases, it is crucial to assure their quality since the early phases of database life cycle. For assessing (and if it is necessary improving) conceptual data model quality it is necessary to dispose of quantitative and objective measures in order to avoid bias in the quality evaluation process. It is in this context that software measurement can help IS designers to make better decision during design activities. The main interest of this article is to provide a state-of-the-art measures for conceptual data models.

This article provides an overview of the basic concepts and state of the art of software measurement. Software measurement is an emerging field of software engineering, since it may provide support for planning, controlling, and improving the software development process, as needed in any industrial development process. Due to the human-intensive nature of software development and its relative novelty, some aspects of software measurement are probably closer to measurement for the social sciences than measurement for the hard sciences. Therefore, software measurement faces a number of challenges whose solution requires both innovative techniques and borrowings from other disciplines. Over the years, a number of techniques and measures have been proposed and assessed via theoretical and empirical analyses. This shows the theoretical and practical interest of the software measurement field, which is constantly evolving to provide new, better techniques to support existing and more recent software engineering development methods.

Improving field performance of telecommunication systems is a key objective of both telecom suppliers and operators, as an increasing amount of business critical systems worldwide are relying on dependable telecommunication. Early defect detection improves field performance in terms of reduced field failure rates and reduced intrinsic downtime. Cost-effective software project management will focus resources towards intensive validation of those areas with highest criticality. This article outlines techniques for identifying such critical areas in software systems. It concentrates on the practical application of criticality-based predictions in industrial development projects, namely the selection of a classification technique and the use of the results in directing management decisions. The first part is comprehensively comparing and evaluating five common classification techniques (Pareto classification, classification trees, factor-based discriminant analysis, fuzzy classification, neural networks) for identifying critical components. Results from a large-scale industrial switching project are included to show the practical benefits. Knowing which technique should be applied to the second area gains even more attention: What are the impacts for practical project management within given resource and time constraints? Several selection criteria based on the results of a combined criticality and history analysis are provided together with concrete implementation decisions.

The use of empirical data to understand and improve software products and software engineering processes is gaining ever increasing attention. Empirical data from products and processes izs needed to help an organization understand and improve its way of doing business in the software domain. Additional motivation for collecting and using data is provided by the need to conform to guidelines and standards which mandate measurement, specifically the SEI's Capability Maturity Model and ISO 9000-3. Some software engineering environments (SEEs) offer automated support for collecting and, in a few cases, using empirical data. Measurement will clearly play a significant role in future SEEs. The paper surveys the trend towards supporting measurement in SEEs and gives details about several existing research and commercial software systems.

Because highly reliable software is becoming an essential ingredient in many systems, software developers apply various techniques to discover faults early in development, such as more rigorous reviews, more extensive testing, and strategic assignment of key personnel. Our goal is to target reliability enhancement activities to those modules that are most likely to have problems. This paper presents a methodology that incorporates genetic programming for predicting the order of software modules based on the expected number of faults. This is the first application of genetic programming to software engineering that we know of. We found that genetic programming can be used to generate software quality models whose inputs are software metrics collected earlier in development, and whose output is a prediction of the number of faults that will be discovered later in development or during operations. We established ordinal evaluation criteria for models, and conducted an industrial case study of software from a military communications system. Case study results were sufficiently good to be useful to a project for choosing modules for extra reliability enhancement treatment.