The term scalability appears frequently in computing literature, but it is a term that is poorly defined and poorly understood. The lack of a clear, consistent and systematic treatment of scalability makes it difficult to evaluate claims of scalability and to compare claims from different sources. This paper presents a framework for precisely characterizing and analyzing the scalability of a software system. The framework treats scalability as a multi-criteria optimization problem and captures the dependency relationships that underlie typical notions of scalability. The paper presents the results of a case study in which the framework and analysis method were applied to a real-world system, demonstrating that it is possible to develop a precise, systematic characterization of scalability and to use the characterization to compare the scalability of alternative system designs.

The term scalability appears frequently in computing literature, but it is a term that is poorly defined and poorly understood. The lack of a clear, consistent and systematic treatment of scalability makes it difficult to evaluate claims of scalability and to compare claims from different sources. This paper presents a framework for precisely characterizing and analyzing the scalability of a software system. The framework treats scalability as a multi-criteria optimization problem and captures the dependency relationships that underlie typical notions of scalability. The paper presents the results of a case study in which the framework and analysis method were applied to a real-world system, demonstrating that it is possible to develop a precise, systematic characterization of scalability and to use the characterization to compare the scalability of alternative system designs.

Scalability is widely recognized as an important software quality, but it is a quality that historically has lacked a consistent and systematic treatment. To address this problem, we recently presented a framework for the characterization and analysis of software systems scalability. That initial work did not provide means to instantiate the variables and functions to be used in the analysis, which could compromise its results. This risk can be mitigated through a systematic exploration of system scalability goals in the application domain during requirements engineering. This paper describes our application of goal-oriented requirements engineering (GORE) for eliciting the scalability requirements of a large, real-world financial fraud detection system. The case study reveals both the suitability and the limitations of GORE as a technique for eliciting the information needed by stakeholders to specify scalability goals of a system. In the paper, we describe these findings in detail and chart a course for future research in extending goal-oriented techniques to scalability requirements.

Generative Programming methods provide some signijcant advantages for the repeated deployment of product line architectures. This paper considers XML as a tool for building and describing applications that use Generative Programming methods. It describes techniques for the creation of a Generative Framework, presents a case study and discusses the results of practical application of these methods in a real world, enterprise scale, product line architecture. The paper presents the advantages of using an XML descriptor that can be easily transformed to generate both static and dynamically conjgurable sofnyare components for direct deployment in an application framework. Two implementation approaches are considered: an indirect approach using XSL for the transformations; and a direct approach where the XML descriptor is parsed and dealt with programmatically. The relative advantages of these two approaches are discussed. m e paper provides practical examples and presents lessons learned from the application of the techniques.

Business Intelligence (BI) systems address the demands of large scale enterprises for operational analytics, management information and decision support tasks. Building such applications presents many challenges. They must support complex and changing data models, have fast turnarounds, present an up-to-date and accurate view of information and provide extensibility mechanisms for new analyses. Widely adopted distributed object systems, such as J2EE can be heavyweight and inflexible when applied to the described scenario. This paper presents our experience when developing a data analysis system that applies a combination of lightweight distributed component technologies available for Java. These technologies are combined in an event-based architecture that anticipates constant changes to analysis algorithms in short time frames and provides the ability to maintain correlated analyses in a consistent state. The resulting architecture is extensible, easy to deploy, highly configurable and has a very flexible data model. We compare this approach with existing distributed object systems and evaluate its suitability to provide business intelligence.