他们为什么要相信我们?决定论,非决定论和证据

D. Budgen
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引用次数: 1

摘要

只提供摘要形式。在软件工程中,就像在计算科学中一样,我们教授给学生的主题可以分为两大类:确定性的和非确定性的。确定性主题是那些特定场景或操作导致的结果可以根据真/假值进行评估的主题,因此这种分类包含了计算机体系结构、数据库、度量和测试的大型元素。然而,软件工程的知识体系实际上关注的是更多的不确定性过程,如需求引出、设计、构建、维护等。在这些活动中,人类发挥着核心作用,做出价值判断,通过使用某种形式的好/坏排名,而不是通过真/假分类,更恰当地评估结果。在我们的教学中,我们在多大程度上认识到这种区别的存在是一个有争议的问题。我们的许多学生,受过经典科学范式的教育,将熟悉导致确定性元素结果的推理类型。在我的演讲中,我考察了一些原因,为什么当他们遇到我们主题的不确定性因素时,这种经验可能不够,因此,为什么我们可能需要灌输对循证范式的某种程度的理解,以支持我们的教学和他们的学习。我将讨论这个范例的本质,介绍一些如何将其用于软件工程的经验,并回顾它提出的一些问题。
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Why should they believe us? Determinism, non-determinism and evidence
Summary form only given. In software engineering, as in computing science, the topics that we teach to our students can be considered as falling into two broad categories: the deterministic, and the non-deterministic. Deterministic topics are those where a specific scenario or operation leads to outcomes that can be assessed in terms of true/false values, and so this classification encompasses large elements of computer architecture, databases, metrics and testing. However, much of the software engineering body of knowledge is really concerned with much more non-deterministic processes such as requirements elicitation, design, construction, maintenance etc. These are activities in which humans play a central role, making value judgements that result in outcomes that are more appropriately assessed by using some form of better/worse ranking than through a true/false categorisation. How much we recognise the existence of this distinction in our teaching is a moot point. Many of our students, educated in the classical science paradigm, will be familiar with the type of reasoning that leads to the outcomes for the deterministic elements. In my presentation, I examine some of the reasons why this experience may not be adequate when they encounter the non-deterministic elements of our subject, and hence why we may need to inculcate some degree of understanding of the evidence-based paradigm in order to support both our teaching and also their learning. I will discuss the nature of this paradigm, present some experiences of how it may be adapted for use in Software Engineering, and review some of the questions that it raises.
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