NFSBST Framework Meta-heuristic perspective

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NFSBST Framework Meta-heuristic perspective by Mind Map: NFSBST Framework Meta-heuristic perspective

1. Genetic Programming/ Multi-objective Optimisation

1.1. Software systems implementing GISMOE approach

1.1.1. Bandwidth, throughput, resource access, execution time C: Measuring Non-functional properties as a fitness function. Problem arises when interpreting these complex properties as a fitness function [P1]. AS: Finding the right metrics that would form the system should be know very well. To do that it is very important to get to know the SUT well. AS: Simulation is recently gaining popularity and can be used to solve this. Cloud-based platforms can also be used. C: Test Data Generation-Analyzing the programs from different perspectives to cover the test cases for the non-functional properties [P1]. AS: It is suggested to fix on a set of properties important for a system and then target them. AS: The use-cases set up for the non-functional properties should be practical.

1.2. Software systems developed in C-language

1.2.1. Execution time C: Small changes in the source code could lead to strange behavior changes to the program's execution time [P4]. AS: Finding techniques that are not invasive (code is not altered during the testing process) C: Maintaining semantic equivalence in the source code during the optimisation process [P4]. AS: GISMOE framework has been developed to address this issue. It is a good idea to use that framework. AS: Program slicing techniques like  ORS have successfully captured data dependencies in a program. They can be used to ensure the correctness of the results. AS: Usage of Coevolution algorithms is recommended [P4].

1.3. Embedded Systems

1.3.1. Performance

1.3.2. Testability

2. Mulit-objective Ant Colony Optimisation

2.1. Component based systems in Automotive domain

2.1.1. Reliability, cost, response time C: Applying Redundant allocation to improve the reliability of the system brings in negative impact on other non-functional attributes [P8]. AS: Solution lies out in trying out Multi-objective optimization algorithms. Additional overheads incur into the response time of the system which itself is an important attribute for automobiles.

3. Particle Swarm Optimisation

3.1. Web-service composition (Service-oriented system)

3.1.1. reliability, availability,execution time C: Finding a right balance between the behavior of the algorithm during search space formulation and the right search space is difficult [P2]. AS: Experimenting different search methods could help in overcoming this issue. AS: Using Hybrid approaches could potentially solve this issue. Refer [P3]