Keecle: Mining key architecturally relevant classes using dynamic analysis

Liliane do Nascimento Vale, M. Maia
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引用次数: 9

Abstract

Reconstructing architectural components from existing software applications is an important task during the software maintenance cycle because either those elements do not exist or are outdated. Reverse engineering techniques are used to reduce the effort demanded during the reconstruction. Unfortunately, there is no widely accepted technique to retrieve software components from source code. Moreover, in several architectural descriptions of systems, a set of architecturally relevant classes are used to represent the set of architectural components. Based on this fact, we propose Keecle, a novel dynamic analysis approach for the detection of such classes from execution traces in a semi-automatic manner. Several mechanisms are applied to reduce the size of traces, and finally the reduced set of key classes is identified using Naive Bayes classification. We evaluated the approach with two open source systems, in order to assess if the encountered classes map to the actual architectural classes defined in the documentation of those respective systems. The results were analyzed in terms of precision and recall, and suggest that the proposed approach is effective for revealing key classes that conceptualize architectural components, outperforming a state-of-the-art approach.
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Keecle:使用动态分析挖掘关键架构相关类
在软件维护周期中,从现有软件应用程序重构体系结构组件是一项重要的任务,因为这些元素要么不存在,要么过时了。逆向工程技术用于减少重建过程中所需的工作量。不幸的是,目前还没有一种被广泛接受的从源代码中检索软件组件的技术。此外,在一些系统的体系结构描述中,一组与体系结构相关的类被用来表示一组体系结构组件。基于这一事实,我们提出了Keecle,一种新的动态分析方法,用于以半自动的方式从执行轨迹中检测这些类。应用了几种机制来减小轨迹的大小,最后使用朴素贝叶斯分类识别关键类的约简集。我们用两个开放源码系统评估了这种方法,以便评估遇到的类是否映射到那些各自系统的文档中定义的实际体系结构类。结果在精确度和召回率方面进行了分析,并表明所建议的方法对于揭示概念化架构组件的关键类是有效的,优于最先进的方法。
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