物件導向軟體之架構(Object-Oriented Software Construction)探討

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

A first course in stochastic processes

Digital forensics research: The next 10 years

Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.

Hierarchical Clustering for Software Architecture Recovery

Determining the impact of requirement changes on software development is critical to project management. We present an impact analysis method to evaluate requirement changes for software development projects that is based on requirements traceability. By using attributes of the work products and traces, we create classes of requirement changes prioritized according to the potential impact. We present a case study that shows a favorable comparison between the actual impact and the predicted impact. Finally, we discuss the expansion of the method.

Analyzing the impact of changing requirements

The activity of reengineering and maintaining large legacy systems involves the use of design recovery techniques to produce abstractions that facilitate the understanding of the system. We present an approach to design recovery based on data mining. This approach derives from the observation that data mining can discover unsuspected non-trivial relationships among elements in large databases. This observation suggests that data mining can be used to elicit new knowledge about the design of a subject system and that it can be applied to large legacy systems. We describe the ISA methodology which uses data mining to identify data cohesive subsystems. We were able to decompose COBOL systems into subsystems by using this approach. Our experience shows that data mining can identify data cohesive subsystems without any previous knowledge of the subject system. Furthermore, data mining can produce meaningful results regardless of system size making this approach especially appropriate to the analysis of large undocumented systems.

Identification of data cohesive subsystems using data mining techniques

Building on ideas from a virtual workshop and additional input from the scientific community, the CISE Directorate at the National Science Foundation established the Information Technology Workforce Program (ITWF) in March 2000 to support a broad set of scientific research studies focused on the under-representation of women and minorities in the information technology workforce. In this paper, we explore various approaches that the funded researchers are taking to address the problem of women in information technology. We begin with a brief history of the ITWF, and then focus on some of the research projects in terms of their goals, approaches, and expected outcomes.

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Shortchanging the future of information technology: the untapped resource

Reengineering legacy systems for distributed environments

Decomposing a software system into subsystems is an important activity in the reverse engineering and maintenance domains. One approach to subsystem decomposition is based on data mining techniques. The ISA (Identification of Subsystems based on Associations) methodology decomposes a software system into subsystems by mining association rules from a database representation of the target system. This paper defines a visual representation model (RM) to graphically present the information produced by ISA. RM provides a graphical view of the recovered subsystem decomposition, the subsystems components, and the relationships among the subsystems. RM includes notation to represent hierarchies of file implications and hierarchies of subsystems. Results of applying RM to represent the subsystem decomposition of a COBOL system are discussed.

Doris L. Carver

Papers

Analyzing the impact of changing requirements

Identification of data cohesive subsystems using data mining techniques

Shortchanging the future of information technology: the untapped resource

Reengineering legacy systems for distributed environments

A visual representation model for software subsystem decomposition