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Search-Based Software Engineering (SBSE) is a promising paradigm thatexploits the computational search to optimize different processes whenengineering complex software systems. Self-adaptive system (SAS) is onecategory of such complex systems that permits to optimize different functionaland non-functional objectives/criteria under changing environments (e.g.,requirements and workload), which involves problems that are subject to search.In this regard, over years, there has been a considerable amount of work thatinvestigates SBSE for SASs. In this paper, we provide the first systematic andcomprehensive survey exclusively on SBSE for SASs, covering papers in 27 venuesfrom 7 repositories, which eventually leads to several key statistics from themost notable 74 primary studies in this particular field of research. Ourresults, surprisingly, have revealed five disappointments that are of utmostimportance and can result in serve consequences but have been overwhelminglyignored in existing studies. We provide theoretical and/or experimentalevidence to justify our arguments against the disappointments, presentsuggestions, and highlight the promising research opportunities towards theirmitigation. We also elaborate on three other emergent, but currentlyunder-explored opportunities for future work on SBSE for SASs. By mitigatingthe disappointments revealed in this work, together with the highlightedopportunities, we hope to be able to excite a much more significant growth inthis particular research direction.

Search-Based Software Engineering for Self-Adaptive Systems: Survey, Disappointments, Suggestions and Opportunities