A combined approach for concern identification in KDM models

Abstract

Abstract Background Systems are considered legacy when their maintenance costs raise to unmanageable levels, but they still deliver valuable benefits for companies. One intrinsic problem of this kind of system is the presence of crosscutting concerns in their architecture, hindering its comprehension and evolution. Architecture-driven modernization (ADM) is the new generation of reengineering in which models are used as main artifacts during the whole process. Using ADM, it is possible to modernize legacy systems by remodularizing their concerns in a more modular shape. In this sense, the first step is the identification of source code elements that contribute to the implementation of those concerns, a process known as concern mining. Although there exist a number of concern mining approaches in the literature, none of them are devoted to ADM, leading individual groups to create their own ad hoc proprietary solutions. In this paper, we propose an approach called crosscutting-concern knowledge discovery meta-model (CCKDM) whose goal is to mine crosscutting concerns in ADM context. Our approach employs a combination of a concern library and a K-means clustering algorithm. Methods We have conducted an experimental study composed of two analyses. The first one aimed to identify the most suitable levenshtein values to apply the clustering algorithm. The second one aimed to check the recall and precision of our approach when compared to oracles and also to two other existing mining techniques (XScan and Timna) found in literature. Results The main result of this work is a combined mining approach for KDM that enables a concern-oriented modernization to be performed. As a secondary and more general result, this work shows that it is possible to adapt existing concern mining code-level approaches for being used in ADM processes and maintain the same level of precision and recall. Conclusions By using the approach herein presented, it was possible to conclude the following: (i) it is possible to automate the identification of crosscutting concerns in KDM models and (ii) the results are similar or equal to other approaches.