NFSBST Framework Requirements perspective

Get Started. It's Free
or sign up with your email address
NFSBST Framework Requirements perspective by Mind Map: NFSBST Framework Requirements perspective

1. Power/ Energy Consumption

1.1. MiniSAT Boolean satisfiability solver

1.1.1. Genetic Improvement

1.1.1.1. C: Capturing the energy consumed outside the CPU proves to be challenging [P5].

1.1.1.1.1. AS: Simulation can partly address this issue.

1.1.1.2. C: Connection between source code and the energy consumed during project compilation [P5].

1.1.1.2.1. AS: Simulation can partly address this issue.

1.2. Systems using GIGI4 framework

1.2.1. Genetic Improvement

1.2.1.1. C: While Using the GIGI4 framework finding other factors that affect the energy consumption and their relative contribution is a challenge [P6].

1.2.1.1.1. AS: The energy consumption equation should be formulated inclusive of the parameters that determine the way the devices are used [P6].

1.3. Micro-Controllers

1.3.1. Genetic Programming

1.4. Software systems in general

1.4.1. Genetic Improvement

1.4.1.1. C: Optimization process is highly susceptible to compiler generated errors [P7].

1.4.1.1.1. AS: Issue could be overcome by involving a penalty and then stopping the program from going to next iteration [P7].

2. Reliability

2.1. Cloud Systems

2.1.1. Genetic Algorithms (GA), Tabu Search (TS), Simulated Annealing (SA)

2.2. Service Oriented systems (Web Service composition)

2.2.1. Particle Swarm Optimisation

2.2.1.1. C:  Finding a right balance between the behavior of the algorithm during search space formulation and the right search space is difficult [P2].

2.2.1.1.1. AS: Experimenting different search methods could help in overcoming this issue.

2.2.1.1.2. AS: Using Hybrid approaches could potentially solve this issue. Refer [P3]

2.3. Systems developed using JAVA

2.3.1. Multiobjective Optimisation Genetic Algorithms(MOGA)

2.4. Component based systems in Automotive domain

2.4.1. Multi-objective ant colony optimisation

2.4.1.1. C: Applying Redundant allocation to improve the reliability of the system brings in negative impact on other non-functional attributes [P8].

2.4.1.1.1. AS: Solution lies out in trying out Multi-objective optimization algorithms.

2.4.1.2. Additional overheads incur into the response time of the system which itself is an important attribute for automobiles.

3. Service Time

3.1. Randomised Algorithms

3.1.1. Genetic Algorithms and multi-objective optimisation

3.1.1.1. C: When using model checking verifying properties of larger systems is very expensive as the number of states grow exponentially with the problem size [P9].

3.1.1.1.1. AS: Always select a subset of the non-functional property set for the optimization

4. Execution Time

4.1. Service Oriented systems (Web Service composition)

4.1.1. Particle Swarm Optimisation

4.1.1.1. C:  Finding a right balance between the behavior of the algorithm during search space formulation and the right search space is difficult [P2].

4.1.1.1.1. AS: Experimenting different search methods could help in overcoming this issue.

4.1.1.1.2. AS: Using Hybrid approaches could potentially solve this issue. Refer [P3]

4.2. Systems optimised using GISMOE framework

4.2.1. Genetic Programming

4.2.1.1. C: Measuring Non-functional properties as a fitness function. Problem arises when interpreting these complex properties as a fitness function [P1].

4.2.1.1.1. 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.

4.2.1.1.2. AS: Simulation is recently gaining popularity and can be used to solve this. Cloud-based platforms can also be used.

4.2.1.2. C:  Test Data Generation-Analyzing the programs from different perspectives to cover the test cases for the non-functional properties [P1].

4.2.1.2.1. AS: It is suggested to fix on a set of properties important for a system and then target them.

4.2.1.2.2. AS: The use-cases set up for the non-functional properties should be practical.

4.3. Systems developed using C

4.3.1. Genetic Programming and Multi-objective optiisation

4.3.1.1. C: Small changes in the source code could lead to strange behavior changes to the program's execution time [P4].

4.3.1.1.1. AS: Finding techniques that are not invasive (code is not altered during the testing process)

4.3.1.2. C:  Maintaining semantic equivalence in the source code during the optimization process [P4].

4.3.1.2.1. AS: GISMOE framework has been developed to address this issue. It is a good idea to use that framework.

4.3.1.2.2. 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.

4.3.1.2.3. AS: Usage of Coevolution algorithms is recommended [P4].

4.4. Micro-Controllers

4.4.1. Genetic Programming