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Automatically Detecting and Tracking Inconsistencies in Software Design Models

机译:自动检测和跟踪软件设计模型中的不一致之处

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摘要

Software models typically contain many inconsistencies and consistency checkers help engineers find them. Even if engineers are willing to tolerate inconsistencies, they are better off knowing about their existence to avoid follow-on errors and unnecessary rework. However, current approaches do not detect or track inconsistencies fast enough. This paper presents an automated approach for detecting and tracking inconsistencies in real time (while the model changes). Engineers only need to define consistency rulesȁ4;in any languageȁ4;and our approach automatically identifies how model changes affect these consistency rules. It does this by observing the behavior of consistency rules to understand how they affect the model. The approach is quick, correct, scalable, fully automated, and easy to use as it does not require any special skills from the engineers using it. We evaluated the approach on 34 models with model sizes of up to 162,237 model elements and 24 types of consistency rules. Our empirical evaluation shows that our approach requires only 1.4 ms to reevaluate the consistency of the model after a change (on average); its performance is not noticeably affected by the model size and common consistency rules but only by the number of consistency rules, at the expense of a quite acceptable, linearly increasing memory consumption.
机译:软件模型通常包含许多不一致性,一致性检查器可以帮助工程师找到它们。即使工程师愿意容忍不一致的情况,他们也最好知道他们的存在,以避免后续错误和不必要的返工。但是,当前的方法不能足够快地检测或跟踪不一致性。本文提出了一种自动化的方法,用于实时检测和跟踪不一致性(当模型更改时)。工程师只需要定义一致性规则ȁ4;可以用任何语言ȁ4定义;我们的方法会自动识别模型更改如何影响这些一致性规则。它通过观察一致性规则的行为来了解它们如何影响模型来做到这一点。该方法快速,正确,可扩展,完全自动化并且易于使用,因为它不需要使用它的工程师的任何特殊技能。我们在34个模型中评估了该方法,模型大小最大为162,237个模型元素和24种一致性规则。我们的经验评估表明,我们的方法仅需要1.4毫秒即可重新评估更改后的模型一致性(平均);它的性能不受模型大小和通用一致性规则的明显影响,而仅受一致性规则的数量的影响,但以相当可接受的,线性增加的内存消耗为代价。

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