1. Genetic Programming/ Multi-objective Optimisation
1.1. Software systems implementing GISMOE approach
1.1.1. Bandwidth, throughput, resource access, execution time
1.1.1.1. C: Measuring Non-functional properties as a fitness function. Problem arises when interpreting these complex properties as a fitness function [P1].
1.1.1.1.1. 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.
1.1.1.1.2. AS: Simulation is recently gaining popularity and can be used to solve this. Cloud-based platforms can also be used.
1.1.1.2. C: Test Data Generation-Analyzing the programs from different perspectives to cover the test cases for the non-functional properties [P1].
1.1.1.2.1. AS: It is suggested to fix on a set of properties important for a system and then target them.
1.1.1.2.2. 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
1.2.1.1. C: Small changes in the source code could lead to strange behavior changes to the program's execution time [P4].
1.2.1.1.1. AS: Finding techniques that are not invasive (code is not altered during the testing process)
1.2.1.2. C: Maintaining semantic equivalence in the source code during the optimisation process [P4].
1.2.1.2.1. AS: GISMOE framework has been developed to address this issue. It is a good idea to use that framework.
1.2.1.2.2. 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.
1.2.1.2.3. 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
2.1.1.1. C: Applying Redundant allocation to improve the reliability of the system brings in negative impact on other non-functional attributes [P8].
2.1.1.1.1. AS: Solution lies out in trying out Multi-objective optimization algorithms.
2.1.1.2. 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
3.1.1.1. C: Finding a right balance between the behavior of the algorithm during search space formulation and the right search space is difficult [P2].
3.1.1.1.1. AS: Experimenting different search methods could help in overcoming this issue.
3.1.1.1.2. AS: Using Hybrid approaches could potentially solve this issue. Refer [P3]