Managing models in a consistent manner is an important task in the field of Model-Driven Engineering (MDE). Although restoring and maintaining consistency is desired in general, recent work has pointed out that always strictly enforcing consistency at any point of time is often not feasible in real-world scenarios, and sometimes even contrary to what a user expects from a trustworthy MDE tool. The challenge of tolerating inconsistencies has been discussed from different viewpoints within and outside the modelling community, but there exists no structured overview of existing and current work in this regard. In this paper, we provide such an overview to help join forces tackling the unresolved problems of tolerating inconsistencies in MDE. We follow the standard process of a Systematic Literature Review (SLR) to point out what tolerance means, how it relates to uncertainty, which examples for tolerant software systems have already been discussed, and which benefits and drawbacks tolerating inconsistencies entails. Furthermore, we propose a tool-chain that helps conducting SLRs in computer science and also eases the reproduction of results. Relevant meta-data of the collected sources is uniformly described in a textual modelling language and exported to the graph database Neo4j to query aggregated information.
翻译:以一致的方式管理模型是模型开发工程领域的一项重要任务。虽然总的来说需要恢复和保持一致性,但最近的工作表明,在现实世界的情景中,始终严格地在任何时刻执行一致性往往不可行,有时甚至与用户对可靠的模型开发工具的期望背道而驰。从建模界内外的不同观点讨论了容忍不一致的挑战,但对这方面的现有和当前工作没有结构化的概览。在本文件中,我们提供了这样的概览,以帮助联合各种力量,解决MDE中容忍不一致的未决问题。我们遵循系统文学审查的标准进程,指出容忍度意味着什么,它与不确定性有什么关系,宽容软件系统的例子已经讨论过,容忍不一致的优点和缺点意味着什么。此外,我们提出一个工具链,帮助在计算机科学方面开展SRL,并便利复制结果。所收集的源的相关元数据在文本模拟语言中统一描述,并出口到图表数据库Neo4j,以查询汇总信息。