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
Contemporary system maturity assessment approaches have failed to provide robust quantitative system evaluations resulting in increased program costs and developmental risks.Standard assessment metrics,such as Technol...Contemporary system maturity assessment approaches have failed to provide robust quantitative system evaluations resulting in increased program costs and developmental risks.Standard assessment metrics,such as Technology Readiness Levels（TRL）,do not sufficiently evaluate increasingly complex systems.The System Readiness Level（SRL）is a newly developed system development metric that is a mathematical function of TRL and Integration Readiness Level（IRL） values for the components and connections of a particular system.SRL acceptance has been hindered because of concerns over SRL mathematical operations that may lead to inaccurate system readiness assessments.These inaccurate system readiness assessments are called readiness reversals.A new SRL calculation method using incidence matrices is proposed to alleviate these mathematical concerns.The presence of SRL readiness reversal is modeled for four SRL calculation methods across several system configurations.Logistic regression analysis demonstrates that the proposed Incidence Matrix SRL（IMSRL）method has a decreased presence of readiness reversal than other approaches suggested in the literature.Viable SRL methods will foster greater SRL adoption by systems engineering professionals and will support system development risk reduction goals.展开更多
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.
文摘Contemporary system maturity assessment approaches have failed to provide robust quantitative system evaluations resulting in increased program costs and developmental risks.Standard assessment metrics,such as Technology Readiness Levels（TRL）,do not sufficiently evaluate increasingly complex systems.The System Readiness Level（SRL）is a newly developed system development metric that is a mathematical function of TRL and Integration Readiness Level（IRL） values for the components and connections of a particular system.SRL acceptance has been hindered because of concerns over SRL mathematical operations that may lead to inaccurate system readiness assessments.These inaccurate system readiness assessments are called readiness reversals.A new SRL calculation method using incidence matrices is proposed to alleviate these mathematical concerns.The presence of SRL readiness reversal is modeled for four SRL calculation methods across several system configurations.Logistic regression analysis demonstrates that the proposed Incidence Matrix SRL（IMSRL）method has a decreased presence of readiness reversal than other approaches suggested in the literature.Viable SRL methods will foster greater SRL adoption by systems engineering professionals and will support system development risk reduction goals.
