To solve the identification and comprehension problem of crosscutting concerns in existing legacy software system, a framework of aspect-oriented software reveme engineering is proposed. An approach on re-modularizing...To solve the identification and comprehension problem of crosscutting concerns in existing legacy software system, a framework of aspect-oriented software reveme engineering is proposed. An approach on re-modularizing traversal features of legacy system is presented based on various unified modeling language （UML） diagrams. While modeling crosscutting concerns in UML use case diagrams, the non-functional requirements that affect several use case modules can be enveloped into aspect modules with a stereotype mechanism. The recurring message transmission patterns can be re-modularized as aspects in UML sequence diagrams with UML collaborations. Standard UML activity diagram notations are extended and modified by node fusion and addition, which support the graphical composition operation between crosscutting behaviors and primary business roles of concurrent systems. Case study indicates that travernal features of software system can be extracted and re-modularized from various perspectives in aspect-oriented reverse engineering, which improves comprehensibility and maintainability of legacy systems.展开更多
Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-o...Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.展开更多
基金Project supported by National Natural Science Foundation of China (Grant No .60473063)
文摘To solve the identification and comprehension problem of crosscutting concerns in existing legacy software system, a framework of aspect-oriented software reveme engineering is proposed. An approach on re-modularizing traversal features of legacy system is presented based on various unified modeling language （UML） diagrams. While modeling crosscutting concerns in UML use case diagrams, the non-functional requirements that affect several use case modules can be enveloped into aspect modules with a stereotype mechanism. The recurring message transmission patterns can be re-modularized as aspects in UML sequence diagrams with UML collaborations. Standard UML activity diagram notations are extended and modified by node fusion and addition, which support the graphical composition operation between crosscutting behaviors and primary business roles of concurrent systems. Case study indicates that travernal features of software system can be extracted and re-modularized from various perspectives in aspect-oriented reverse engineering, which improves comprehensibility and maintainability of legacy systems.
基金Supported by the National Pre-research Project (513150601)
文摘Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.
