# Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm
## 5 Discussion
### State-of-the-Art Comparison  
We contrast SPEA2-SAS with the following cutting-edge search-based frameworks from the literature to assess its efficacy further:  
PLATO [9] is an optimization strategy that uses the famous single-objective evolutionary algorithm genetic algorithm together with a weighted sum of the objectives. Additionally, it does not take dependence into account while optimizing, thus we employ the same method as SPEA2-SAS. To identify the most effective solution, we assign the objectives equal weights. We chose elitist chromosome representations as PLATO relies on manual transposition to make comparisons that are fair and remove bias in repeatability.  
FUSION [11] is a framework that uses SAS optimization as an integer programming issue using a model-based approach with an aggregate objective function that is solved using an exact algorithm (in the tests, we employ branch-and-bound). FUSION solely takes into account category dependence and employs a binary representation of the solutions. As a result, when there is no viable option, we correct the numerical dependence violations.  
SPEA2-SAS achieves competitive results on runtime overhead in comparison to state-of-the-art frameworks, but the actual time required by SPEA2-SAS depends on the underlying MOEA. Notably, FUSION has the highest overhead because its exact algorithm cannot scale with a large search space, and the optimization runs are frequently forcibly returned. SPEA2-SAS's ability to develop synergy between SAS software engineering and evolutionary computing is its most prominent advantage. Software programmers are given the freedom to modify an MOEA's behaviors in whatever way they are accustomed to (i.e., the feature) without in-depth knowledge of evolutionary algorithms in general.  
SPEA2-SAS makes use of MOEA [35], which is particularly appropriate in situations where the concerned weights of the objectives are uncertain and also challenging to define. We do admit that there are situations where the relative importance of competing goals is stated explicitly. In such instances, the strength of elitist chromosome representation and dependency-aware operators may still be utilized by the SAS. It is also important to note that MOEA does not ensure optimum solutions, but it is particularly effective at generating accurate approximations to difficult, nonlinear problems that would otherwise be impractical to solve by exact optimization. Therefore, we do not advocate utilizing the SPEA2-SAS approach for SAS that is straightforward, has a small search area, and can be resolved with an exact search that produces the best solutions.  
To tackle our SAS optimization challenge, however, search-based software engineering methods, notably evolutionary algorithms, present a potential approach. This is because of the algorithms' dynamic nature, which allows them to undertake optimization without requiring in-depth knowledge of or presumptions about the issue at hand (such as the SAS's characteristics or the environment). Additionally, it is possible to develop roughly optimal solutions for even the most difficult situations because of the concepts of natural evolution and population.