# Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm
## 6 Conclusions  
To optimize SAS at runtime, this article introduces SPEA2-SAS, a unique framework that automatically combines a SAS feature model with an MOEA in a systematic way. To build a chromosome representation, SPEA2-SAS identifies elitism characteristics at design time, including categorical and numerical ones. It then extracts gene dependencies, which are subsequently utilized to expand the underlying MOEA for runtime optimization. The engineers' domain expertise, the feature model, may narrow the search space and guide the search, boosting the likelihood of coming up with better solutions. SPEA2-SAS further identifies optimal solutions that produce a balanced tradeoff between two objectives. We also demonstrate how SPEA2-SAS produces better and more balanced results for tradeoffs with reasonable overhead by thoroughly contrasting it with its variants and cutting-edge frameworks on two complex real-world SAS with different parameters like mutation, crossover values, etc. In particular, the most noteworthy findings of SPEA2-SAS include the following; (1) While using the elitist chromosome representation to encode an issue into MOEA can enhance quality and reduce runtime overhead for SAS optimization, such a gain is more likely to be modest when there are few viable options, like when the workload is high in terms of a large number of features of a SAS, (2) The search may be adequately guided by the dependency-aware operators, who will discover solutions with greater convergence and diversity metrics like hypervolume as mentioned in the above sections, producing superior SAS optimization. However, using dependency-aware operators without making sure the chosen adaptation method is balanced may reduce its efficacy, (3) The choice of SAT solver aids in locating a more sensible adaptation strategy in terms of a valid test case suite, (4) SPEA2-SAS, with the tested SPEA2, delivers statistically and practically better quality for SAS runtime optimization than the state-of-the-art frameworks from the literature. The proposed work influences and develops the synergy between evolutionary computing and software engineering for SAS, incorporating the best aspects of both disciplines. Software developers may use MOEAs to solve SAS optimization without having substantial prior MOEA experience, especially with SPEA2-SAS. On the other hand, automating the feature model's conversion into an MOEA context can help to learn MOEA and create domain expertise understandably and systematically for academics studying MOEA, which can help in designing SAS algorithms that are more effective. As opposed to many search-based software engineering (SBSE) problems, our deeper synergy extends the internal structure of MOEA by automatically and dynamically retrieving SAS domain knowledge. In subsequent work, we want to enhance SPEA2-SAS to manage additional competing objectives by applying it to more SAS domains. We believe that combining techniques like the one we have described with approaches from a different category will have a similar positive impact on quality assurance for SAS. According to what we understand, this is especially true for SAS, where runtime surprises may occur and need to be properly handled. However, testing must still be carried out before a system is put into use to guarantee at least a foundational level of SAS quality. The proposed testing method can be developed in many ways in the future. Instead, we may utilize coverage criteria and a formal model to automatically generate test inputs and oracles taking SAS and its environment's unpredictability into account. Our proposed work has contributed to the multi-objective research area in the following ways.