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The establishment and application of a metric for graphical design language (abstract)

机译:图形设计语言度量的建立和应用(摘要)

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摘要

The concept of a graphical design language as a tool for implementing problem solving and algorithm design independent of any specific code language is extremely attractive with reference to initial instruction in algorithm design. The verification of the methodology requires an acceptable reliable metric to validate that the graphical design language approach does improve the ability of problem solvers. Current software engineering metrics have primarily concentrated on attempting to predict development overhead or to evaluate the final software product.

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Using an environment (DATA) (Doran & Longenecker 1989) (Doran & Longenecker 1990) which focuses on the crucial role of data in the algorithm design process, the goal remains the establishment of an instrument to test the algorithm design skills of problem solvers trained with DATA versus those trained using traditional methods (flow charting, pseudo-code, Nassi-Schneiderman charts, etc.). A stable metric applicable to graphical design languages in general is an obvious requirement. The approach to developing the metric involves application of the prototype instrument to groups of students through a series of courses aimed at bringing the students through problem solving and algorithm design to programmed implementation of algorithms. The performance of the groups will provide the data for calibrating the metric validating its reliability. The metric is then to be used to measure the impact on performance of the DATA teaching environment. In the papers cited, the need for the DATA environment and the apparent impact have been demonstrated. The natural next phase calls for the development of the metric.

机译:相对于算法设计中的初始指令,作为独立于任何特定代码语言来实现问题解决和算法设计的工具的图形设计语言的概念极具吸引力。方法论的验证需要一个可接受的可靠指标,以验证图形设计语言方法确实可以提高问题解决者的能力。当前的软件工程指标主要集中在尝试预测开发开销或评估最终软件产品。 rn

使用环境(DATA)(Doran&Longenecker 1989)(Doran&Longenecker 1990),重点是数据在算法设计过程中起着至关重要的作用,目标仍然是建立一种工具来测试用DATA培训的问题解决者与使用传统方法(流程图,伪代码,Nassi-Schneiderman图表,等等。)。通常,适用于图形设计语言的稳定指标很明显。制定度量标准的方法包括通过一系列课程将原型仪器应用到学生群体,旨在使学生通过问题解决和算法设计达到算法的编程实现。小组的表现将为校准指标提供数据,以验证其可靠性。然后,将使用该度量来衡量对DATA教学环境性能的影响。在引用的论文中,已经证明了对DATA环境的需求和明显的影响。自然而然的下一阶段需要制定度量标准。

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