Different computation problem domains such as sorting, matrix multiplication, etc. usually require different data representations and algorithms variants implementations in order to be adapted and re-designed to context-aware composition (CAC). Context-aware composition is a technique for the design of applications that can adapt its behavior according to changes in the program. We considered two application domains: matrix multiplication and graph algorithms (DFS algorithm in particular). The main problem in the implementation of the representation mechanisms applied in these problem domains is time spent on the data representation conversion that in the end should not influence the application performance.        This thesis work presents a flexible aspect-based architecture that includes the data structure representation adaptation in order to reduce implementation efforts required for adaptation different application domains.      Although, manual approach has small overhead 4-10% for different problems compared to the AOP-based approach, experiments show that the manual adaptation to CAC requires on average three times more programming effort in terms of lines of code than AOP-based approach. Moreover, the AOP-based approach showed the average speed-up over baseline algorithms that use standard data structures of 2.1.