12043862
A method and tool support for variant requirements analysis: goal and scenario based approach
Identifying and representing variant requirements among products in a product family are crucial activities for a successful software reuse. The variant requirements should be not only identified based on the business goal, which drives marketing plan, product plan, and differences among products, but also represented as familiar notations in order to support developing a particular product in the product family. Thus, our proposal is to identify the variant requirements through goals and scenarios, and represent them as variable use cases for a product family. Especially, for identification of the variant requirements, we propose four abstraction levels of requirements in the product family, and goal and scenario modeling. For representation of them, variable use case model is suggested, and also the use case transfer rules are proposed so as to bridge the gap between the identification and representation activity. The paper illustrates the application of the approach within a supporting tool, namely DREAM (Domain REquirements Analysis Method) using the ATM example.
16878992
Improving use case driven analysis using goal and scenario authoring: A linguistics-based approach
Use case driven analysis does not facilitate effective requirements elicitation or provide rationale for the various artifacts that get generated during the requirements analysis phase. On the other hand, goal and scenario based approach is considered to be effective for elicitation but it does not lead to use cases. This paper discusses how to combine goal and scenario based requirements elicitation technique with use case driven analysis using natural language concepts. In our proposed approach, four levels of goals, scenario authoring rules, and linguistic techniques have been developed to identify use cases from text based goal and scenario descriptions. The artifacts resulting from our approach could be used as input to a use case diagramming tool to automate the process of use case diagram generation.
28523270
Goal based agent-oriented software modeling
This paper presents a goal based modeling method for an agent-oriented software system. In modeling research for agent-oriented software, problems often emerged in that the properties of an agent system were not efficiently reflected in the development phase (analysis, design, etc.), because the techniques based on the other software paradigm are adopted to develop agent-oriented software. The result of analysis differs depending on which paradigm is used. An agent is goal-oriented, which necessitates a shift in paradigm. Therefore, the modeling method for an agent system should start from the concept of goal.
35666646
A Linguistics-Based Approach for Use Case Driven Analysis Using Goal and Scenario Authoring
Although Use Case driven analysis has been widely used in requirements analysis, it does not facilitate effective requirements elicitation or provide rationale for the various artifacts that get generated. On the other hand, goal and scenario based approach is considered to be effective for elicitation but it does not lead to use cases. This paper discusses how to combine goal and scenario based requirements elicitation technique with use case driven analysis using natural language concepts. In our proposed approach, four levels of goals, scenario authoring rules, and linguistic techniques have been developed to identify use cases from text based goal and scenario descriptions. The artifacts resulting from our approach could be used as input to a use case diagramming tool to automate the process of use case diagram generation.
2757542
Comparing the performance of high-level middleware systems in shared and distributed memory parallel environments
The utilization of toolkits for writing parallel and/or distributed applications has been shown to greatly enhance developer's productivity. Such an approach hides many of the complexities associated with writing these applications, rather than relying solely on programming language aids and parallel library support, such as MPI or PVM. In this work, we evaluate three different middleware systems that have been used to implement a computation and I/O-intensive data analysis application from the domain of computer vision. This study shows the benefits and overheads associated with each of the middleware systems, in different homogeneous computational environments and with different workloads. Our results lead the way toward being able to make better decisions for tuning the application environment, for selecting the appropriate middleware, and also for designing more powerful middleware systems to efficiently build and run highly complex applications in both parallel and distributed computing environments.
14570877
Genetic Elevator Group Control
In a tall building with multiple elevators, it is a notoriously di cult task to control the elevators in the most e cient manner [1].The system proposed in this paper contains two key ideas. First, it continuously generates dispatch functions by adaptation to changes in passenger tra c. Second, it tries to reduce passengers' waiting time by multiple elevator allocation when it is expected that one elevator cannot serve all the passengers for a hall call at a oor.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.
27298271
The Rule-Based Approach to Reconfiguration of 2-D Processor Arrays
The authors propose to set up assignment rules based on the available interconnection resources of 2-D processor arrays. The reconfiguration of 2-D processor arrays is guided by the assignment rules, such that logical cells are always connected through the available interconnection resources. The advantage of the proposed approach is the easy adaptation to various system requirements, by adjusting assignment rules. The proposed reconfiguration algorithm can be extended to arrays with clustered faulty cells, overcoming the weakness of fixed domain approaches. >
14157019
Projections of Binary Linear Codes onto Larger Fields
We study certain projections of binary linear codes onto larger fields. These projections include the well-known projection of the extended Golay [24,12,8] code onto the hexacode over $\mbox{GF}(4)$ and the projection of the Reed--Muller code R(2,5) onto the unique self-dual [8,4,4] code over $\mbox{GF}(4)$. We give a characterization of these projections, and we construct several binary linear codes which have best known optimal parameters, for instance, [20,11,5], [40,22,8], [48,21,12], and [72,31,16]. We also relate the automorphism group of a quaternary code to that of the corresponding binary code.