In component-based software development, it is necessary to measure the reusability of components in order to realize the reuse of components effectively. There are some product metrics for measuring the reusability of Object-Oriented software. However, in application development with reuse, it is difficult to use conventional metrics because the source codes of components cannot be obtained, and these metrics require analysis of source codes. In this paper, we propose a metrics suite for measuring the reusability of such black-box components based on limited information that can be obtained from the outside of components without any source codes. We define five metrics for measuring a component's understandability, adaptability, and portability, with confidence intervals that were set by statistical analysis of a number of JavaBeans components. Moreover, we provide a reusability metric by combining these metrics based on a reusability model. As a result of evaluation experiments, it is found that our metrics can effectively identify black-box components with high reusability.
Social insects perform complex tasks without top-down-style control, by sensing and depositing chemical markers called "pheromone". We have examined applications of this pheromone paradigm towards realizing intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risks of overload, bottlenecks and delay. Our work points towards a more decentralized approach that may overcome those risks. We use new category of the ITS infrastructure called the probe-car system. The probe-car system is an emerging data collection method, in which a number of vehicles are used as moving sensors to detect actual traffic situations. In this paper, a car is regarded as a social insect that deposits multi-semantics of (digital) pheromone on the basis of sensed traffic information. We have developed a basic model for predicting traffic congestion in the immediate future using pheromone. In the course of our experimentation, we have identified the need to properly tune the model to achieve acceptable performance. Therefore, we refined the model for practical use. We evaluate our method using real-world traffic data and results indicate applicability to prediction. Furthermore, we describe the practical implications of this method in the real world.
Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques; however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.
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