Automated Improvement of Software Architecture Models for Performance and Other Quality AttributesQuality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques. |
Common terms and phrases
additional Additionally AllocationContext analysis antipattern applied architect architectural candidate AssemblyContext automated improvement BookingSystem candidate model CBA metamodel CBA model CBML change type component allocation component selection Computer configuration considered context costs crossover defined definition degrees of freedom described design space DoFI dominance EMOF evaluation evolutionary algorithms evolutionary optimization example Figure framework freedom instances function hardware hyper-volume implementation input instantiated interfaces iteration Koziolek metaheuristic metamodel elements method middleware model elements multiobjective optimization mutation Object Management Group optimization approach optimization problem parameters Pareto dominance Pareto front Pareto-optimal passive resources performance model PerOpteryx POFOD ponent primary changeable element processing rate processor quality attributes quality criteria quality metric quality properties quality requirements QuickBooking reliability resource demand Reussner ROBOCOP scenario Section server S1 software architecture model Software Engineering software system solutions specific SubSystem tion trade-off utilization valid workflow Zitzler