基于历史的模型修复建议

Manuel Ohrndorf, Christopher Pietsch, U. Kelter, Lars Grunske, Timo Kehrer
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引用次数: 14

摘要

模型驱动工程中的模型是主要的开发工件,在软件开发的所有阶段都要进行大量编辑,并且在编辑期间可能会暂时不一致。一般来说,解决不一致的方法有很多,哪一种最合适取决于各种因素。正如最近的模型修复方法所建议的那样,将修复方案的实际选择和批准留给开发人员自行决定是合理的。模型修复工具可以通过提出最有希望的修复列表来支持开发人员。这样的修复建议只有在产生的建议是合理和可理解的,并且作为一个整体是可管理的情况下才会在实践中被接受。目前的方法,主要集中在穷尽搜索策略,探索所有可能的模型修复,而不考虑历史变化的意图,不能满足这些要求。在本文中,我们提出了一种新的方法来生成修复建议,该建议针对过去不完整的编辑步骤所引入的不一致,这些步骤可以位于模型的版本历史中。这样一个不完整的编辑步骤要么被撤消,要么被扩展为一个保持一致性的编辑操作的完整执行。基于历史的不一致性分析以及修复建议的生成都是完全自动化的,所有的交互式选择步骤都由我们的修复工具REVISION支持。我们使用从Eclipse Git存储库中托管的流行开源建模项目中获得的真实模型的历史来评估我们的方法,包括整个UML元模型的演变。我们的实验结果证实了我们的假设,即大多数不一致,即93.4,可以通过补充不完整的编辑来解决。92.6%的修复建议是相关的,因为它们的影响可以在模型的历史中观察到。94.9%的相关修复方案排在首位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
History-based Model Repair Recommendations
Models in Model-driven Engineering are primary development artifacts that are heavily edited in all stages of software development and that can become temporarily inconsistent during editing. In general, there are many alternatives to resolve an inconsistency, and which one is the most suitable depends on a variety of factors. As also proposed by recent approaches to model repair, it is reasonable to leave the actual choice and approval of a repair alternative to the discretion of the developer. Model repair tools can support developers by proposing a list of the most promising repairs. Such repair recommendations will be only accepted in practice if the generated proposals are plausible and understandable, and if the set as a whole is manageable. Current approaches, which mostly focus on exhaustive search strategies, exploring all possible model repairs without considering the intention of historic changes, fail in meeting these requirements. In this article, we present a new approach to generate repair proposals that aims at inconsistencies that have been introduced by past incomplete edit steps that can be located in the version history of a model. Such an incomplete edit step is either undone or it is extended to a full execution of a consistency-preserving edit operation. The history-based analysis of inconsistencies as well as the generation of repair recommendations are fully automated, and all interactive selection steps are supported by our repair tool called REVISION. We evaluate our approach using histories of real-world models obtained from popular open-source modeling projects hosted in the Eclipse Git repository, including the evolution of the entire UML meta-model. Our experimental results confirm our hypothesis that most of the inconsistencies, namely, 93.4, can be resolved by complementing incomplete edits. 92.6% of the generated repair proposals are relevant in the sense that their effect can be observed in the models’ histories. 94.9% of the relevant repair proposals are ranked at the topmost position.
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