.png){kind=icon}
.png){kind=icon}
Return
Multilevel component selection optimization toward an optimal architecture
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-016-2472-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Component-based software engineering uses components to construct systems, being a means to increase productivity by promoting software reuse. This work deals with the component selection problem. A multiobjective optimization approach is used to formulate the problem. The contribution of the current paper is threefold: the multilevel decomposition approach, the use of both functional and nonfunctional requirements (cost of a component, thus cost of the solution as sum of the costs of the constituent components) as objectives, and the use of metrics values to evaluate the architecture of the obtained solutions. As advantages over the existing literature we mention: the automatic computation of component interactions and of the constituent components for each module, multiple solutions obtained in a single run, and the new architecture evaluation step based on metrics values that is not present in other approaches. The decomposition offers valuable insights about internal structure of the system which led us to identify metrics to assess coupling and cohesion of the architecture design. The internal structure influences the external quality; thus, a highly cohesive module exhibits high reusability and loosely coupled systems enable easy maintenance. In this context, when selecting the best solution out of a set of available ones, we aim to maximize the cohesion of modules and to minimize the coupling between them, obtaining thus the best reusable and maintenable solution. To evaluate our approach, we discuss a case study for building a reservation system. In order to obtain the best parameters to run the evolutionary approach, three different studies were applied. The tests performed show the potential of evolutionary algorithms for this particular problem and for other similar ones.
Keywords:
Component selection
Architecture assessment
Optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W