# Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm
## 2 Related Work  
Some state-of-the-art works have been discussed in this section, which becomes a motivation for our proposed approach.  
By storing copies on servers at physically distant locations, remote data mirroring (RDM) is a data security approach to ensure data availability and reduce data loss [22; 23].  
The RDM network must also efficiently replicate and disseminate data by minimizing bandwidth consumption and offering assurance that dispersed data are not lost or corrupted. In reaction to uncertainties, such as dropped or delayed messages and network link failures, the RDM can be reconfigured in real-time. Each network link also has a measurable throughput, latency, and loss rate, as well as operational costs that have an impact on a controlling budget. These indicators are used to assess the overall effectiveness and dependability of the RDM. The RDM can change its network topology and data mirroring algorithms to address unforeseen problems. The RDM application can be described and executed as a SAS because of its sophisticated and adaptable nature [24].  
Another approach is adopted by [8], where using the elitist chromosomal representation to encode a problem into MOEA can improve quality and reduce runtime overhead for SAS optimization. Such a gain is more likely to be modest when there are few viable solutions. The search may be adequately guided by the dependency-aware operators, who will discover solutions with greater convergence and diversity, producing better SAS optimization. Nevertheless, using dependency-aware operators without making sure the selected adaptation method is balanced may reduce its efficacy. The FEMOSA approach uses different MOEAs to generate better quality for SAS runtime optimization compared to the leading frameworks in the literature. Comparing FEMOSA to modern frameworks, it has a very low runtime overhead overall. Additionally, the additional work required for knee selection and dependency-aware operators is minimal; on occasion, they can even marginally shorten the MOEA runtime [8].  
Proteus methodology is a runtime testing management based on requirements. Proteus is a framework specifically designed to execute testing tasks at runtime, such as test execution and online adaptation. Proteus offers two layers of test adaptation to achieve this: test case parameter value adaptation and test suite adaptation. The management of runtime testing activities, such as the modification and execution of test cases and test suites, is handled by the Proteus framework. Proteus carries out two primary jobs to aid in this strategy. Each SAS configuration has an adaptive test plan specified at design time, where each adaptive test plan consists of numerous test suites and each SAS configuration corresponds to a specific operational scenario. Second, each time a new SAS configuration is run, Proteus conducts a testing cycle that may include numerous iterations that each run a distinct test suite. Next, each task is outlined individually [25].  
Our proposed approach can affect SAS and evolutionary algorithms, incorporating the best aspects of both disciplines. Researchers can use MOEAs to handle SAS optimization without having substantial prior MOEA experience, especially with the SPEA2 method. The automatic conversion of the feature model into an MOEA context can establish systematic and understandable domain knowledge for evolutionary computation researchers, who would be able to generate more novel results in terms of the SAS domain. As opposed to many search-based software engineering problems, our comprehensive methodology extends the internal structure of MOEA by automatically and dynamically retrieving SAS domain knowledge. Future work will expand SPEA2-SAS to manage additional competing objectives and apply it to other SAS domains.