EA-MDE: An Empirical Assessment of the Efficacy of Model Driven Engineering

软件在社会中的复杂性和普遍性正呈指数级增长。人们普遍认为，管理这种复杂性并继续向公众提供软件利益的唯一现实的方法是使用适当的抽象方法开发软件。今天，最先进的软件抽象是模型驱动工程（MDE）——也就是说，在软件工程过程中系统地使用模型作为主要工件。MDE包括各种模型驱动的软件开发方法，包括模型驱动的体系结构、特定于领域的建模和模型集成的计算。尽管MDE声称有许多潜在的好处——主要是在生产力、可移植性、可维护性和互操作性方面的好处——但它的开发在很大程度上没有经验数据的支持来测试这些说法。因此，是否使用MDE的决定主要基于专家意见，而不是硬实证数据；而这些观点经常出现分歧。缺乏实证结果的MDE是一个问题，原因有两个。首先，业界在MDE工具的开发和应用上投入了数百万美元。如果没有经验证据证明这些工具的有效性，就有浪费资源的危险。无论当前MDE工具的品牌是否成功，抽象模型的概念对软件的未来至关重要。需要进行经验评估，以确保未来的软件工具符合软件开发人员的思维方式。其次，学术界也以博士论文和研究论文的形式对MDE进行了大量投资。这项研究很少有经验证据，这意味着资助机构很难正确评估研究结果的有用性。一个问题是，早期职业研究人员缺乏本质上需要的多学科知识——即MDE的知识和进行实验的心理学知识。另一个简单的问题是，MDE的经验评估很难。严格的评估应该在很长一段时间内让工业界参与进来，但工业界往往不愿意参与进来，因为它看不到新的研究技术的直接好处。拟议的研究将通过开发一个基于当前行业实践和需求的mde实证评估框架来解决这两个问题，并可供研究人员在必要时使用和调整。本研究的总体目标是解决一些基本问题，如如何将实证评估与MDE研究和实践最好地结合起来，并为MDE评估和采用提供科学基础。这是一个为期12个月的试点项目。这是出于以下原因。对MDE进行经验评估的挑战从根本上来说是困难的。由于这方面的研究很少，因此在投入更多资源之前进行可行性研究是有益的。我们将利用这一试验阶段，规划MDE评估前景，并制定初步评估框架。我们将该试点项目视为为MDE评估提供科学基础的两阶段工作的第一阶段。我们计划使用试点的结果来支持后续的第二阶段工作，该工作将提出：软件建模者认知过程的理论；更详细的评价框架；以及新一代MDE工具。

The complexity and pervasiveness of software in society is growing exponentially. It is generally agreed that theonly realistic way to manage this complexity, and to continue to provide software benefits to the public at large, is todevelop software using appropriate methods of abstraction. Today, the state-of-the-art in software abstraction ismodel-driven engineering (MDE) - that is, the systematic use of models as primary artefacts during a softwareengineering process. MDE includes various model-driven approaches to software development, including model-drivenarchitecture, domain-specific modelling, and model-integrated computing.Although MDE claims many potential benefits - chiefly, gains in productivity, portability, maintainability andinteroperability - it has been developed largely without the support of empirical data to test these claims. As a result,decisions whether or not to use MDE are based mainly on expert opinion rather than hard empirical data; and theseopinions often diverge. The lack of empirical results on MDE is a problem for two reasons.Firstly, industry invests millions in the development and application of MDE tools. Without empiricalevidence of the efficacy of these tools, there is a danger that resources are being wasted. Whether or not the currentbrand of MDE tools succeeds, the notion of abstract models is crucial to the future of software. Empirical evaluationsare needed to ensure that future software tools will match the way that software developers think.Secondly, academia also invests significantly in MDE in the form of PhD theses and research papers. This research israrely informed by empirical evidence, which means that it is difficult for funding bodies to properly assess theusefulness of research results. One issue is that early-career researchers lack the multidisciplinary knowledge that isinherently required - that is, knowledge both of MDE and the psychological know-how of conducting experiments.Another problem is simply that empirical evaluation in MDE is hard. Rigorous evaluations ought to engage industryover lengthy periods of time, but industry is often reluctant to get involved because it cannot see immediate benefits of anovel research technique.The proposed research will address these two problems by developing a framework for empirical evaluation of MDEthat is informed by current industry practice and needs and is available to researchers to use and adapt it as necessary.The overall goal of the research is to address fundamental questions as to how empirical evaluations can best beintegrated with MDE research and practice as well as to provide steps toward a scientific foundation for MDEevaluation and adoption.This is a 12 month pilot project. This is for the following reason. The challenges of empiricallyevaluating MDE are fundamentally hard. Since there has been very little research in this area, a feasibility study wouldbe beneficial before investing further resources. We will use this pilot phase to map out the MDE evaluation landscapeand to develop an initial evaluation framework. We view the pilot as phase I of a two phase effort to provide a scientificfoundation for MDE evaluation. We plan to use the results of the pilot to support a follow-on phase II effort that willpropose: a theory of software modellers' cognitive processes; a more detailed evaluation framework; and a newgeneration of MDE tools.