Self-Adaptive Systems

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TABLE 1 TABLE 3 Words after self- Words after self-organizing

Word Frequency Word Frequency organizing 2,981 maps 706 adaptive 1,682 map 582 stabilizing 632 neural 165 organization 564 feature 109 organized 515 networks 79

TABLE 2 TABLE 4 Words after autonom* Words after self-adaptive

Word Frequency Word Frequency vehicles 576 systems 159 mobile 554 differential 77 driving 407 software 76 systems 399 and 31 agents 399 evolutionary 27

enabling reproducibility of results. All results in this review was deemed as acceptable. Disagreements were resolved were produced using the snapshot file dblp-2020-03-02.xml. after confirming any ambiguities and biases. This step re- The methodology is broken down into five main stages: Pre- sulted in a list of words to exclude from the final data set. filtering, word frequency filtering, venue selection, abstract After omitting papers with the excluded phrases in the title, reading, and snowballing to reduce a first subset of 35,903 the number of publications reduced to 20,137. publications to 293 publications. The titles of the 20,137 publications were manually scanned for relevance, resulting in 872 articles. We again 2.1 Pre-Filtering assessed the validity of this step using Cohen’s κ by having two authors identify relevant papers in a randomly selected The dblp data containing 4,964,345 papers was filtered to subset of 50 papers. The value calculated was 0.73 which include only journal articles and papers published in confer- was deemed acceptable. ence proceedings, with a page count of over five to focus our analysis on substantial and fully evaluated research contributions which appeared in peer-reviewed venues. Capital- 2.3 Venue Selection ization was removed to assist with the filtering process. This As a quality gate, we only considered papers published in established the base dataset. The base dataset was searched A*/A conferences or journals, as determined by the CORE with grep for two terms: self-* and autonom-* to produce ranking.1 46% of all journals and 55% of all conferences two separate datasets. These two keywords were deemed listed by CORE are ranked as A*/A. Papers published in as broadly relevant to the study area. The total number of B-ranked venues were included if they came from a journal publications matching these keywords was 35,903. or conference that was relevant to the study area. This step reduced the number of candidate papers to 298. 2.2 Word Frequency Filtering Since the number of papers was too large to categorize man- 2.4 Abstract Reading and Attribute Matrix ually, we further filtered papers based on additional words The abstracts of the 298 candidate papers were then read in the paper titles. We first determined the distribution of to categorize each paper into one of five categories based words after the keywords self-* and autonom*. Tables 1 off the paper categorization introduced by the ICSE 2014 and 2 show the distribution of the five most frequent words conference:2 for self-* and for autonom*. • Because we observed that self-organizing and self- Analytical: A paper in which the main contribution adaptive frequently co-occurred with terms irrelevant to relies on new algorithms or mathematical theory. • self-adaptive systems (such as self-organizing teams), we Empirical: A paper in which the main contribution conducted a second word frequency analysis to identify is the empirical study of a software engineering common words appearing after self-organizing and self- technology or phenomenon. • adaptive. The top results are shown in Tables 3 and 4. Technological: A paper in which the main contribu- Two of the authors manually and independently an- tion is of a technical nature. alyzed all words occurring after ‘self-’, ‘autonom*’, ‘self- 1. The CORE rankings http://portal.core.edu.au/conf-ranks/ and organizing’, and ‘self-adaptive’ at least 20 times to indicate http://portal.core.edu.au/jnl-ranks/ are maintained by the Comput- those that were out of scope (such as self-organizing teams) ing Research and Education Association of Australasia and are used for exclusion. To ensure the validity of this process, we world-wide. κ 2. The categories published in the ICSE 2014 call for papers at calculated inter-rater reliability using Cohen’s , see Table 5. https://2014.icse-conferences.org/research provide one of the most The Cohen’s κ value was greater than 0.7 in all cases which comprehensive categorization schemes for software-related papers. 3

TABLE 5 TABLE 6 Inter-rater agreement Papers per category

Dataset Cohen’s κ Category Count Autonom* 0.828 Technological 105 Self-* 0.748 Methodology 79 Self-organizing * 1.000 Perspective 51 Self-adaptive * 1.000 Analytical 35 Empirical 23 50 randomly sampled titles 0.730 Total 293

• Methodological: A paper in which the main contribution is a coherent system of broad principles and tributions (26%) and frameworks (14%). Like other practices to interpret or solve a problem. categories, the number of case studies vs. simulations • Perspectives: A paper in which the main contribution follows roughly a 60/40 split. A substantial number is a novel perspective on the field as a whole, or part of papers are extensions of previous work (31%), and thereof. almost all (94%) papers provide a formalization. In terms of evaluation methods, the largest group are Once each paper was categorized, an attribute matrix quantitative (37%), followed by case studies (29%) for each category was developed. Within each category, the and comparisons (17%). abstract and a pass of the full paper was read to develop • Empirical: Most of the empirical papers focus on run- an understanding of the common types of attributes for a time (74%) instead of design-time, with an explicit category. This was done in an iterative approach. Once the monitoring (83%) and/or adaptation (87%) strategy. attribute matrix was finalized, all the papers from each cat- Studies again follow the 60/40 split between case egory were categorized using the matrix. This step resulted studies and simulations. The adaptation techniques in 28 papers being discarded as irrelevant. Note that each focus on parameters (87%) rather than components category has its own attribute matrix since not all attributes (9%), and evaluation is mostly quantitative (57%) or apply to all categories. The complete matrices with the done through case studies (39%). characterizations of all papers are shown at the end of the paper and summarized here: We further identified the application domain of each paper, if applicable. The application domains are shown in • Technological: 81% of papers in this category follow the attribute matrices at the end of the paper and the top a closed circle approach as opposed to human in 10 most common application domains are summarized in the loop, and implementations cover tools (24%), Table 7. models (23%), frameworks (22%), languages, architectures, and algorithms. 60% of the papers can be 2.5 Snowballing characterized as case studies, the remaining 40% rely on simulations. The vast majority of contributions To capture additional papers, snowballing was used on the are novel (87%), compared to a few extensions of papers included in the attribute matrix. Where possible, previous work. The methods for evaluation range each paper was added to a Scopus list where the references from quantitative approaches (44%) and case studies were extracted automatically. This step captured 90% of the (35%) to comparisons (15%). papers in the matrix. Any paper that was referenced more • Methodology: The majority of papers in this category than five times was eligible to be considered. These papers (70%) introduce new approaches for self-adaptive were then filtered through the same criteria as the original systems, followed by frameworks (15%), analysis papers from the dblp step (i.e., abstracts, CORE rankings). techniques (8%), and architectures (4%). The ratio of From this step, 23 new papers were added. These papers simulations (20%) is lower than in the Technological were categorized into the five categories following the same category, and the vast amount of papers provide approach as before. The final counts for each category are new contributions (90%) here as well. Evaluation shown in Table 6 and are considered in the ratios reported follows a similar pattern to the Technological cate- in the previous section. gory, with quantitative evaluations (41%) being the most common, followed by case studies (38%) and 3 SELF-ADAPTIVE SYSTEMS OVER THE YEARS comparisons (16%). In the following, we provide a chronological overview of • Perspective: Many of the papers in this category can research on self-adaptive systems based on our system- be classified as reflections (35%), reviews (31%), and atic literature review. We identify trends and summarize surveys (20%), with papers outlining future work contributions and developments in the following five time (71%), challenges (33%), and requirements (16%), or periods: providing a taxonomy (14%). Evaluation methods, if applicable, in this category are mostly focused on • 1990-2002 - Initial Stages, Section 3.1 comparisons (31%) and case studies (29%). • 2003-2005 - Foundational Years, Section 3.2 • Analytical: Many papers in this category focus on • 2006-2010 - Ramping up, Section 3.3 algorithms (46%), followed by mathematical con- • 2011-2015 - The last decade - First Half, Section 3.4 4

TABLE 7 TABLE 9 Papers per application domain Application domains during initial stages

Application Domain Count Application Domain Count Web Services 48 Networking 2 IoT 36 Software Engineering 1 Review 25 IoT 1 Robotics 23 Speech Recognition 1 Networking 19 Robotics 1 IaaS 17 Intelligence Surveillance Reconnaissance 13 Software Engineering 12 Automotive 10 Mobile Systems 10 self-configuration, self-optimization, self-healing, and self- Service-Oriented Systems 10 protection in computing systems. It foretasted the rising need for these systems in the modern age. Shortly after this, an implementation of these concepts was developed, TABLE 8 Categories during initial stages known as the RAINBOW framework [109]. This framework was architecture based and extensible, meaning existing Category Count architectures could be imbued with self-adaptive properties. To this day, the RAINBOW framework is used as a standard, Technological 4 test bed, extensible tool in self-adaptive systems research. Analytical 2 Many papers were published during this time period Total 6 that were in the perspective category (42%). As the ex- citement of a new field grew, many researchers sought to publish their thoughts and ideas as to how the field should • 2016-2020 - The last decade - Second Half, Section 3.5 and could develop. During this period, there were fewer As an orthogonal perspective, the tables at the end of practical demonstrations and implementations compared to this paper group all 293 papers by category and list their future time periods as the field had to have time to grow attributes. and mature. This application domain distribution of this period was 3.1 1990-2002 - Initial Stages skewed towards review papers. Of the eight review papers, four were on autonomic computing [107], [108], [148], [149] According to our literature review, the earliest reference to and four were on self-* properties [12], [64], [78], [120]. The self-adaptive systems was in 1990. The period of 1990-2003 key messages of these papers were that a new challenge was the phase before ‘The Vision of Autonomic Comput- was emerging in the field of autonomic computing and self- ing’ [149], was published. This was where the field was in * computing. Due to the rise in system complexity, it was its beginning stages. necessary to develop a new kind of system that was self- References in this phase contained theoretical and model adaptive. These papers also envisioned what such realized based papers such as a model for dynamic change manage- systems might look like. ment [155], self-stabilizing real-time rule based systems [60], The most immediate application domain for self- and convergence for self-stabilizing systems [25]. These adaptive systems was web services. At the time, technolo- papers were focused on presenting a theoretical model gies such as IaaS and IoT had not fully emerged yet. Web or proving a proposed theorem to advance self-adaptive services were a prominent tool used in software engineer- systems theory. ing. By 1998, practical implementations were seen such as architecture based run-time software evolution [199], self- The improvement of the system administrator role was supervised category detection [291], and self-adaptive con- a focus area of research by improving collaboration and trol software [209]. The papers were among the first to dis- coordination, rehearsal and planning, maintaining situation cuss terms like evolution, self-supervision, control theory, awareness, and managing multitasking, interruptions, and and run-time design in the context of self-adaptive systems. diversions [19]. Utility functions emerged as a potential Thirty years later, these terms are commonplace and part of metric to solve self-adaptive problems [271]. In later years, the general understanding of the field. this would prove to be true. The automatic management Tables 8 and 9 characterize this time period in terms of of web services was an important test bed to develop self- the prevalent paper categories and application domains. adaptive algorithms and theory [126], [145], [290]. There was a major parallel between autonomic computing, self-* computing, and biological systems, and a branch 3.2 2003-2005 - Foundational Years of research was formed to gain inspiration from nature to In the subsequent years after 2002, two seminal works bring to these systems [125], [140], [197]. As a precursor to were produced that formed the foundations of self-adaptive the cloud based systems of the present day, load balanc- systems. In 2003, The Vision of Autonomic Computing [149] ing was an area of research focus. Work forecasting [28], was published which kick-started the field of Autonomic scheduling algorithms [159], and managing system level Computing. The paper presented a grand challenge of properties [287] were all part of the groundwork of this 5

TABLE 10 with good results [61]. In the latter half of this period, the Categories during foundational years focus of web services grew to become more holistic with concepts such as architectural self-reconfiguration and self- Category Count tuning [72], [113], behavioral adaptations [142], expansion Perspective 14 into more self-* properties [143], and adaption logic based Technological 8 approaches [210]. The emphasis was on adaptation of the Methodology 5 entire system as opposed to individual components. Analytical 3 Empirical 3 IoT papers were initially focused on solvers [65], [191] Total 33 with a key driver being dynamic behavior [45], [127]. Fault tolerance [127] and multi-agent models [65] were important themes to establish in IoT due to their distributed and TABLE 11 Application domains during foundational years always-on nature. A tool called RELAX was developed to handle requirements and uncertainty in a smart home self- Application Domain Count adaptive system domain [288]. Requirements and uncertainty are important in any self-adaptive system, not just Review 8 IoT systems [236]. The requirements of the system determine Web Services 6 Load Balancing 3 the goals of the adaptations. Appropriately managing uncer- Bio-inspired 3 tainty gives a system flexibility and dynamism. As with web IoT 3 services, a holistic approach to self-adaptation using archi- Networking 2 Software Engineering 2 tectural self-reconfiguration was a part of the research [76]. Adaption logic in IoT was addressed using heuristics in availability and response time [176] and in a tool called research area, as was the placement of replicants in an edge FUSION [92]. The research efforts of IoT compared with web computing scenario [244]. services in this phase share a parallel. Initially, solvers and IoT and IaaS papers were present during this time. languages were created and tested, then various research Concepts like autonomous deployment [178], generic con- concepts were explored like distributed solutions for web trol frameworks [144], and self-healing distributed architec- services or fault tolerance for IoT. Then, a shift in focus tures [242] were explored, as was the concept of self-aware to more holistic approaches to self-adaptability were em- systems [7] in general. These two fields would emerge as phasized such as architectural self-reconfiguration, or the strong research fields in later years. adaptation logic. Other research areas include Robotics [301], Automo- This phase was the first time robotics was seen as tive [79], Computer [248], Software Stack [289], Network- an application domain with seven papers. Robotics rep- ing [39], [119], Software Engineering [174], and Intelligence resented a high potential area for the application of self- Surveillance Reconnaissance [42]. adaptive systems. By their nature, robots are intended to Tables 10 and 11 characterize this time period in terms automatically do the tasks of humans and so self-adaptive of the prevalent paper categories and application domains, robots are a natural progression of the goals. Requirements confirming the large number of perspective papers. and modelling [181], [255] were the early emphasis with a progression to adaptable software architectures [255], [273] 3.3 2006-2010 - Ramping up and self-organized and distributed systems [235]. Tools to The distribution of the years 2006-2010 showed a more even compare adaptation approaches were developed [283]. This spread across the categories. Technological, Perspective, and started to become necessary as the popularity of the field Methodology papers were evenly spread. There was still gave rise to multiple different adaptation techniques. Learn- a high demand for perspective type papers but enough ing and planning was a component of the research of this time had passed since 2003 for technology and methodology phase [87], [257]. Using reinforcement learning, planning papers to emerge. and architecture self-management was explored [87]. The Web services was still the highest polling application learning research theme was in its early stages in this phase. domain but in this period IoT papers surfaced as the In future years, learning would prove to be key to widely second most frequent domain. Web services in this phase used tools like machine and deep learning. focused on the principles and ideas of autonomic and self- This phase also gave rise to nine review papers. Com- adaptive systems developed in the previous phase. These pared to the previous phase, review papers made up a manifested initially in new languages [63], utility func- smaller proportion of the total papers in the group. The tions [63], and distributed solutions as opposed to cen- reviews were conducted on self-organization [146], [192], tralized solutions [175]. There was a clear focus on run autonomic computing [131], [162], [222], self-healing [115], time instead of design time solutions [93], [243] as well as and self-adaptation [91], [231], as well as a general overview making systems dynamic in their configuration and adap- of self-* properties [30]. The common theme of this work tation [20], [31], [204]. Self-healing was an important part was to highlight the current work and future needs of the of web services research during this time, highlighting the field. One remark confirms the trend highlighted above of use in workarounds [54] and composition cycles [57]. The needing a more holistic approach to self-adaptation: ”but RAINBOW framework was evaluated on a signature case what is missing is an holistic approach focusing explicitly on pro- study: Znn.com to assess the effectiveness of self-adaptation viding autonomic properties” [222]. Written in 2009, it perhaps 6

TABLE 12 ranking with seven papers. This was the first time period Categories during ramping up stage where IaaS made it in to the top domains. This is in line with the timeline of cloud based services as they rose to Category Count prominence. Methodology 23 Web services papers in this time period displayed a Technological 20 slightly different flavor to the previous time periods. Forms Perspective 18 Analytical 8 of validation were more popular in this phase. Perfor- Empirical 6 mance [18] and integration [73] testing, probabilistic model Total 75 checking [51], [69], [183], quality assurance [165], and evaluation [49] indicate that more emphasis was now being put on verifying the outcomes of self-adaptive solutions. It was TABLE 13 Application domains during ramping up stage not enough to claim that a system was self-adaptive, but the claims had to be backed up and tested. Frameworks formed part of the contributions to the research. With the Application Domain Count push towards more holistic self-adaptive solutions, this was Web Services 14 a natural progression. Multi-model [206], dynamic alloca- IoT 9 Review 8 tion [166], monitoring [258], and behavioral [228] frame- Robotics 7 works highlight the variety of holistic generic approaches E-Commerce 3 being explored. The RAINBOW framework still held influ- Service Oriented Systems 3 Automated Traffic Control 3 ence during this period, with a framework called REFRACT Automotive 3 extending RAINBOW to target fault avoidance [252]. Plan- Intelligence Surveillance Reconnaissance 2 ning was a key component of this phase – automatic recon- Mobile Systems 2 figuration plans [203], adapting manager optimization [4], Computer 2 Software Engineering 2 and plan generation techniques [66] all highlight the continued importance of forecasting in self-adaptive systems. Distributed techniques [233], fault localization [55], and explains the trend toward holistic solutions to self-adaptive architecture based self-adaptation [58] all were continued systems in the literature. self-adaptive themes from the previous phase. This phase showed a variety of other application Robotics papers also shared an emphasis on building domains. Decentralized solutions were popular in auto- generic frameworks for self-adaptation. Architectural com- mated traffic control [284], e-Commerce [264], water net- pilers [193], reference models [285], and testing frameworks [88], service oriented systems [47], and load bal- works [101] highlight some of the work done here. There ancing solutions [16]. Learning was a prominent theme was a continued focus on dynamic [194] and run-time [135] for automotive [89], traffic management control [90], and applications as well as verification of systems [137]. The e-Commerce [132]. The ability for a system to adapt its modelling of uncertainty became a strong focus during adaptation logic was a strong area of focus for mobile sys- this phase. As decisions in the system are pushed from tems [300], ISR [254], e-Commerce [9], software engineering design time to run time, the amount of possible outcomes applications [8], [106], and systems on chip [29]. Evalu- for the system dramatically increases. The behavior of the ation [41], [247], self-organization [86], [123], [277], self- system also becomes non-deterministic. Verification [293], protection [163], self-healing [238], resource allocation [2], consequence modelling [229], and latency modelling [183] reflection [5], as well as dynamic solutions [262] were all all attest to this effort. A strong research theme of the explored during this time. Consistent with the theme of robotics domain in self-adaptive systems is goal modelling. holistic solutions, generic architectures were developed in The goals of a robot and associated utility functions are speech recognition tools [154] as well as generic frameworks highly important to successful behavior. Dealing with fuzzy such as SASSY [169]. Other application domains not listed goals [17], interactions [229], and learning [253] all con- include UML [14], Holonic Systems [227], Video Encod- tributed to this focus. ing [22], task scheduling [260], and Physics [245]. Networking was a domain that was well represented Tables 12 and 13 characterize this time period in terms during this time period. With the explosion of the Internet of the prevalent paper categories and application domains, and always-on devices, networking was a ripe domain to underlining that papers in the ‘Methodology’ category and apply self-adaptive principles to. Consistent with the theme about Web Services were becoming increasingly dominant. of frameworks during this time period, mathematical [13], scheduling [261], sensor modelling [21], and testing [104] 3.4 2011-2015 - The last decade - First Half frameworks were created. There was a similar emphasis The first half of the 2010s showed a bimodal result, with on validation [105] and fault tolerance [171]. The RAIN- technological and methodology papers being the most fre- BOW framework was again used as an exemplar during quent. The proportion of perspective papers decreased, per- this phase. It was applied to manage and monitor highly haps attributed to the saturation of them in previous time populated networks of devices [48]. Consistent with the periods. themes from previous phases, self-organization [80] and Web services still dominated the domain distribution, self-reconfiguration [82], [141] were popular research areas followed by robotics and networking. IoT still had a high in networking. 7

TABLE 14 TABLE 15 Categories during ﬁrst half of the last decade Application domains during ﬁrst half of the last decade

Category Count Application Domain Count Technological 37 Web Services 19 Methodology 29 Robotics 11 Empirical 9 Networking 10 Analytical 8 IoT 7 Perspective 8 Intelligence Surveillance Reconnaissance 6 Service Oriented Systems 5 Total 91 Software Engineering 5 Review 5 IaaS 4 Mobile Systems 4 The IoT domain shares a high overlap with the net- Bio-inspired 2 working domain as IoT solutions are essentially localized Control Engineering 2 networks. The trends of the IoT research in this phase is consistent with the themes of generic frameworks [1], [160], [172], [180], [297] and validation [129], [130]. 3.5 2016-2020 - The last decade - Second Half The IaaS domain entered the top domains in this phase. In the second half of the decade its popularity would ex- The second half of the 2010s displayed a similar distribution plode. During this time, IaaS papers focused on regression to the first half, with technological and methodology papers testing [152], control theory [6], decision making [27], [133], being most frequent, followed by analytical, perspective, transaction management [116], and on benchmarking and and empirical. This is not surprising as the developments elasticity [124]. These themes would later on be developed of the field were at a comparable maturation stage. and expanded. The distribution of the domains during this period shows an interesting trend. Web services are no longer the There were only six reviews in this time period, the most frequent domain, rather IoT and IaaS are the most fre- lowest proportion of any time period so far. This could quent domains. There is a distinct trend of these cloud based be due to the reduced need because of the ongoing work. technologies from obscurity (1990-2003) to niche (2003-2010) Reviews in this period focused on self-healing [217], [237], to growth (2011-2015) to now in the 2020s where cloud based self-adaptation [158], [282], self-protection [298], and on services are mainstream. This trend is reflected in the rise of control engineering approaches to self-adaptive system de- IoT and IaaS in the domain distribution of the papers. sign [205]. IoT has become a popular application domain in the last Frameworks in ISR [150], [263], service oriented sys- five years of the 2010s. The number of devices with access tems [43], [173], [267], software engineering [62], [95], and to the Internet has increased exponentially in quantity but mobile systems [99], [114] highlight the trend of the theme also variety. Devices are not just limited to phones and com- in developing holistic solutions to self-adaptive systems. puters but extend to watches, cars, buildings, sensors, and Continued trends are requirements [96], [220], dynamic so- more. The research in this phase gave rise to a number of lutions [208], multi-agent systems [35], [53], and utility [211] exemplars in IoT. Exemplars can be generic such as artifacts as well as reliability [75], with more abstract ideas like or address specific self-adaptive problems. They are used as systems evaluation [265], uncertainty handling [94], [121], a demonstration of a working solution in the problem space. and feedback loops [207], [270] now getting covered. This was the period of time where exemplars began to be Bio-inspired approaches were again present in this widely seen and used. DeltaIoT [134], an evaluation exem- phase. These approaches have the understanding that self- plar and DingNet [216], a simulation exemplar highlight the adaptive systems are much like biological systems and work done. Carrying on from the work done in the previous that there is much inspiration to draw from nature. Pa- time periods, frameworks and generic architectures were pers discuss chemically inspired architectures for reusable seen in HAFLoop [299], decentralized approaches [214], models [259], as well as cloud based applications inspired [278], and modelling frameworks [32]. Behavioral modelling by biological principles [56] and multi-objective control for of IoT behavior was a continued trend [10], [24], [139], self-adaptive software design [98]. These biological inspired [280]. Specifically, emergent behaviors were explored [198]. approaches are a potential growth area for self-adaptive Emergent behavior is an important concept in self-adaptive systems. systems. These behaviors are the byproduct of allowing Other domain applications not listed include e- decisions to be made at run time. When this occurs, the Commerce [232], UML [164], automotive [122], water net- system may display new behavior not previously conceived works [117], automated traffic management [286], fault or seen before. The appropriate handling of these behaviors recovery [275], video encoding [97], application contain- is important to a large scale self-adaptive system like IoT. ers [128], and human participation [71]. Common research focuses like new languages [11], evalu- Tables 14 and 15 characterize this time period in terms ation and testing [70], [221], modelling [85], learning [50], of the prevalent paper categories and application domains, [219], [249], recovery [23], uncertainty [246], and integra- showing a large number of papers in the categories ‘Tech- tion [44] were seen in this phase. A general review of self- nological’ and ‘Methodology’, again dominated by the ap- improving system integration can be found in [26] and plication area of Web Services. industrial experience reports in [161], [250]. 8

IaaS became more of a focus during this period. As it them to be embedded into any physical tool. The success- went further into the decade, cloud based solutions became ful integration [276] and the tracability of these compo- more and more common in business and hence in research. nents [110] are critical to the field. It became cheaper to rent out infrastructure in the cloud Continuing on with the last time period, bio inspired ap- and outsource maintenance costs than to handle everything proaches were seen in this time period, addressing emergent in-house. This is also reflected in research focusing on con- behavior [223] and artificial DNA [40]. Security was a focus current approaches [167], [266] or hierarchical systems [84]. of this theme with guarantees [170] and verifications [46] From the previous time period, the research areas of being explored. The self-protection aspect of self-adaptive testing [212], control theory [214], decision making [100], systems has been sprinkled amongst the time periods (with [153], [201], and elasticity [219] in IaaS were expanded on. works focusing on trust [239], [274], [292] and on situational New areas of research, such as trust [177], structural and awareness [296]), however as these systems gain traction parametric adaptation [157], monitoring [15], [85], mod- and popularity, there will be an increased need to secure elling [59], [230], and service level maintenance [213] were these systems in the same fashion as micro-transactions are established in the field. Trust in self-adaptive systems is an secured in financial institutions. It would not be surprising important concept. Even if the self-adaptation loops are ro- if in the next decade, security became a more prominent bust and effective, without establishing trust for the system, application domain. using these systems in large scale or critical environments There was a paper in this time period on smart factory or is infeasible. This has to to with the understood error rate industry 4.0 [272]. Seen as the next progression in industrial of the self-adaptive system and the tolerance of the user. In activity, this application domain has potential to grow going some cases it may be acceptable to have a 20% error rate in to the next decade. Exemplars were again seen in this in a non-critical scenario but for another critical scenario period across other domains using architectural self-healing like a Defence setting, even a 5% error rate may not be and self-optimization [268]. acceptable given the possible consequences. The research The frequencies of robotics and networking decreased efforts in self-adaptive systems are usually split between in this phase. This could be because they are less popular structural and parametric adaptation. Structural adaptation or that there is some overlap between these domains and involves modifying or improving the components of the the top two domains, IoT and IaaS. The second reason is system whereas parametric adaptation involves optimising more likely. Planning [186], [202], [224], [294], testing [103], the configurable parameters of the system, leaving the com- fault tolerance [33], and uncertainty [102], [182] all highlight ponents unchanged. Addressing both of the styles at once is common research trends seen before in the timelines, as is an area of need and potential [157]. model-predictive control [136]. Mobile systems most likely The research into web services has benefited from a 20 also share the same similarities with overlap as networking plus year build up. Consistent with the trend of this time pe- and robotics to IoT and IaaS. They have been a consistent riod, exemplars were used to demonstrate the capabilities of theme across the timelines and have a presence in this one self-adaptive web services using TCP communication [187]. with dynamic decisions [196], input space mapping [218] Multi-agent systems [37], uncertainty [189], planning [200], and emotion measurement [68]. A review of self-adaptive models [179], [269], and programming concepts [67], [184] systems in the context of mobile systems is given in [118], were all continued research themes into web services. The one on monitoring self-adaptive applications within edge state of web services after 20 plus years has moved from computing frameworks in [256], and one on learning in self- foundational theory to generic frameworks and to exem- adaptive systems in [77]. plars. Even despite this trend, the various research themes are still being explored and mined for use after 20 years The automotive application domain increased in this which indicates that there is more to learn. time period compared to the previous time period. This may Cyber-Physical Systems (CPS) are a combination of be explained by the new found viability of smart cars and computation, networking, and physical processes where a self-driving cars in recent years. Key trends for this domain physical component is controlled by a chip or software were adaptive, scalable, and robust systems. These systems component. This time period is the first time CPS are seen. are proactively aware of latency and can act in swarms [34], This indicates that they are a relatively new research area [52], [81], [151], [188]. to self-adaptive systems. The dominating application (24%) ISR has had a consistent presence across the timelines. to CPS is energy and the dominant adaptation mecha- Resilience [215], goal theory [240], control theory [241], and nism is MAPE-K [190]. A new language, Adaptive CSP assurance [226] highlight the research efforts in this time was developed to support compositional verification of period. ISR is an important application domain to self- systems [36]. Continuing with the trend of exemplars in adaptive systems. It enables real time situational awareness this phase, DARTSim represents a simulation of UAVs on and allows analysts to make decisions based off current and a reconnaissance mission communicating via TCP [185]. useful information. In a Defence context, generating this According to [111], a central concept in these systems is intelligence from data is extremely important to the decision homeostasis, the capacity to maintain an operational state makers. despite run-time uncertainty. This is addressed by four Other application domains included clonal plastic- principles: collaborative sensing, faulty component isolation ity [195], smart travels [138], agriculture [38], UML [74], sys- from adaptation, enhancing mode switching, and adjusting tem on chip [225], MAPE-K [112], traffic management [156], guards in mode switching. CPS are naturally employed holonic systems [83], and managing support of recoonfig- in safety-critical environments as their small nature allows urable software components [3]. 9

TABLE 16 5 LIMITATIONS Categories during second half of the last decade This systematic literature review was conducted with some assumptions. The dblp database was a suitable database to Category Count capture self-adaptive systems. Dblp is a computer science Technological 36 bibliography, and the review would not capture papers Methodology 22 Analytical 14 outside this bibliography in fields like medicine, science, Perspective 11 and other engineering fields. Empirical 5 The application domains mentioned in this paper are Total 88 subject to the interpretation of the papers. A paper may have multiple application domains but only one was chosen TABLE 17 for each paper. This means there is some overlap across the Application domains during second half of the last decade domains. At best, it is useful to get a flavor of the types of papers in self-adaptive systems across the 30 year time Application Domain Count period but it is not a comprehensive survey of all the types of domains. IoT 16 IaaS 12 Web Services 9 Cyber Physical Systems 6 6 CONCLUSION Automotive 5 Review 4 A systematic literature review was conducted using the Mobile Systems 4 Intelligence Surveillance Reconnaissance 4 dblp computer science bibliography. In a series of steps, Robotics 3 the database was filtered to 293 relevant papers on self- Networking 3 adaptive systems. These papers were organized into an Load Balancing 2 attribute matrix of five categories: Analytical, Technological, Service Oriented Systems 2 Software Engineering 2 Methodological, Empirical, and Perspective. The domain of Security 2 each paper was extracted and put in a frequency table across Bio-inspired 2 a 30-year time period and the gradual rise of self-adaptive systems as a research field was characterized.

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TABLE 18 Analytical

Type Applic. Contrib. Formaliz. Evaluation Method Reference Year Algorithm Architecture Framework Mathematical Language Deﬁnition Case study Simulated Extension Novel Yes No Preliminary Case Study Industrial Comparison Human Subject Quantitative Unknown/None Domain [60] 1992 ✓ ✓ ✓ ✓ ✓ Networking [25] 1999 ✓ ✓ ✓ ✓ Networking [39] 2005 ✓ ✓ ✓ ✓ ✓ Networking [290] 2005 ✓ ✓ ✓ ✓ ✓ Web Services [301] 2005 ✓ ✓ ✓ ✓ ✓ Robotics [274] 2006 ✓ ✓ ✓ ✓ ✓ Security [227] 2007 ✓ ✓ ✓ ✓ ✓ Holonic Systems [243] 2008 ✓ ✓ ✓ ✓ ✓ Web Services [245] 2009 ✓ ✓ ✓ ✓ ✓ Physics [30] 2009 ✓ ✓ ✓ ✓ Other [191] 2009 ✓ ✓ ✓ ✓ ✓ IoT [132] 2010 ✓ ✓ ✓ ✓ ✓ e-Commerce [142] 2010 ✓ ✓ ✓ ✓ ✓ Web Services [259] 2011 ✓ ✓ ✓ ✓ ✓ Bio-inspired [220] 2011 ✓ ✓ ✓ ✓ ✓ IoT [13] 2011 ✓ ✓ ✓ ✓ ✓ Networking [95] 2011 ✓ ✓ ✓ ✓ ✓ Software Engineering [96] 2011 ✓ ✓ ✓ ✓ ✓ Service-Oriented Systems [263] 2012 ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [21] 2012 ✓ ✓ ✓ ✓ ✓ Networking [253] 2013 ✓ ✓ ✓ ✓ ✓ Robotics [23] 2016 ✓ ✓ ✓ ✓ ✓ IoT [223] 2016 ✓ ✓ ✓ ✓ ✓ Bio-inspired [177] 2016 ✓ ✓ ✓ ✓ ✓ IaaS [33] 2016 ✓ ✓ ✓ ✓ ✓ Networking [266] 2016 ✓ ✓ ✓ ✓ ✓ Other [202] 2017 ✓ ✓ ✓ ✓ ✓ IaaS [198] 2017 ✓ ✓ ✓ ✓ ✓ ✓ IoT [52] 2018 ✓ ✓ ✓ ✓ ✓ Automotive [153] 2018 ✓ ✓ ✓ ✓ ✓ IaaS [85] 2018 ✓ ✓ ✓ ✓ ✓ IoT [294] 2018 ✓ ✓ ✓ ✓ ✓ Robotics [224] 2018 ✓ ✓ ✓ ✓ ✓ Robotics [170] 2018 ✓ ✓ ✓ ✓ ✓ Security [200] 2019 ✓ ✓ ✓ ✓ ✓ Web Services

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Type Implementation Applic. Contrib. Goals Evaluation Method Reference Year Human in the loop Closed circle Tool Model Framework Language Architecture Algorithm Case Study Simulated Extension Novel Goals Utility Preliminary Case Study Industrial Comparison Human Subject Quantitative Unknown/None Domain [155] 1990 ✓ ✓ ✓ ✓ ✓ Software Engineering [199] 1998 ✓ ✓ ✓ ✓ ✓ IoT [291] 1999 ✓ ✓ ✓ ✓ ✓ Speech Recognition [209] 2000 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [109] 2004 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Software Engineering [159] 2004 ✓ ✓ ✓ ✓ ✓ Load Balancing [178] 2004 ✓ ✓ ✓ ✓ ✓ ✓ IaaS [197] 2004 ✓ ✓ ✓ ✓ ✓ ✓ Bio-inspired [271] 2004 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Web Services [126] 2005 ✓ ✓ ✓ ✓ ✓ Web Services [289] 2005 ✓ ✓ ✓ ✓ ✓ Software Stack [140] 2005 ✓ ✓ ✓ ✓ ✓ Bio-inspired [9] 2006 ✓ ✓ ✓ ✓ ✓ ✓ e-Commerce [31] 2006 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Networking [63] 2006 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [292] 2006 ✓ ✓ ✓ ✓ ✓ Security [254] 2007 ✓ ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [22] 2007 ✓ ✓ ✓ ✓ ✓ Video Encoding [175] 2007 ✓ ✓ ✓ ✓ ✓ Web Services [16] 2008 ✓ ✓ ✓ ✓ ✓ ✓ Load Balancing [127] 2008 ✓ ✓ ✓ ✓ ✓ ✓ IoT [181] 2008 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [14] 2009 ✓ ✓ ✓ ✓ ✓ ✓ UML [288] 2009 ✓ ✓ ✓ ✓ ✓ ✓ IoT [76] 2009 ✓ ✓ ✓ ✓ ✓ ✓ IoT [273] 2009 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [57] 2009 ✓ ✓ ✓ ✓ ✓ Web Services [93] 2009 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [264] 2010 ✓ ✓ ✓ ✓ ✓ e-Commerce [45] 2010 ✓ ✓ ✓ ✓ ✓ ✓ ✓ IoT [283] 2010 ✓ ✓ ✓ ✓ ✓ Robotics [257] 2010 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [75] 2011 ✓ ✓ ✓ ✓ ✓ Programmer [53] 2011 ✓ ✓ ✓ ✓ ✓ ✓ Resource Management [275] 2011 ✓ ✓ ✓ ✓ ✓ Software Stack [180] 2011 ✓ ✓ ✓ ✓ ✓ ✓ ✓ IoT [130] 2011 ✓ ✓ ✓ ✓ ✓ ✓ IoT [193] 2011 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [43] 2011 ✓ ✓ ✓ ✓ ✓ Service-Oriented Systems [173] 2011 ✓ ✓ ✓ ✓ ✓ ✓ Service-Oriented Systems [18] 2011 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [206] 2011 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [166] 2011 ✓ ✓ ✓ ✓ ✓ Web Services [56] 2012 ✓ ✓ ✓ ✓ ✓ Bio-inspired [172] 2012 ✓ ✓ ✓ ✓ ✓ ✓ IoT [150] 2012 ✓ ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [80] 2012 ✓ ✓ ✓ ✓ ✓ ✓ Networking [285] 2012 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [270] 2012 ✓ ✓ ✓ ✓ ✓ Software Engineering [62] 2012 ✓ ✓ ✓ ✓ ✓ ✓ Software Engineering [122] 2013 ✓ ✓ ✓ ✓ ✓ Automotive [27] 2013 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Other [114] 2013 ✓ ✓ ✓ ✓ ✓ ✓ Mobile Systems [137] 2013 ✓ ✓ ✓ ✓ ✓ Robotics [128] 2013 ✓ ✓ ✓ ✓ ✓ Software Engineering [258] 2013 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [160] 2014 ✓ ✓ ✓ ✓ ✓ IoT [1] 2014 ✓ ✓ ✓ ✓ ✓ Mobile Systems [101] 2014 ✓ ✓ ✓ ✓ ✓ Robotics [135] 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Robotics [17] 2014 ✓ ✓ ✓ ✓ ✓ ✓ Robotics [58] 2014 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [252] 2014 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [71] 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [35] 2015 ✓ ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [171] 2015 ✓ ✓ ✓ ✓ ✓ ✓ Networking [233] 2015 ✓ ✓ ✓ ✓ ✓ Web Services [4] 2015 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [183] 2015 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Web Services [272] 2016 ✓ ✓ ✓ ✓ ✓ Smart Factory [11] 2016 ✓ ✓ ✓ ✓ ✓ ✓ IoT [139] 2016 ✓ ✓ ✓ ✓ ✓ IoT [215] 2016 ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [196] 2016 ✓ ✓ ✓ ✓ ✓ ✓ Mobile Systems [102] 2016 ✓ ✓ ✓ ✓ ✓ Networking [167] 2016 ✓ ✓ ✓ ✓ ✓ Software Engineering [67] 2016 ✓ ✓ ✓ ✓ ✓ ✓ Web Services [184] 2016 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Web Services 12 TABLE 20 Technological (2/2)

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TABLE 21 Perspective

Type Content Evaluation Method Reference Year Survey Review Evaluation of framework Reflection Roadmap Comparison Taxonomy Challenges Future Work Requirements Preliminary Case Study Industrial Comparison Human Subject Quantitative Unknown/None Domain [107] 2003 ✓ ✓ ✓ ✓ ✓ Review [125] 2003 ✓ ✓ ✓ Bio-inspired [149] 2003 ✓ ✓ ✓ ✓ ✓ Review [7] 2004 ✓ ✓ ✓ IoT [19] 2004 ✓ ✓ ✓ Web Services [108] 2004 ✓ ✓ ✓ Review [119] 2004 ✓ ✓ ✓ Networking [120] 2004 ✓ ✓ ✓ Review [78] 2004 ✓ ✓ ✓ Review [174] 2004 ✓ ✓ ✓ ✓ ✓ Software Engineering [12] 2005 ✓ ✓ ✓ Review [64] 2005 ✓ ✓ ✓ ✓ Review [148] 2005 ✓ ✓ ✓ Review [248] 2005 ✓ ✓ ✓ Computer [192] 2006 ✓ ✓ ✓ Review [162] 2006 ✓ ✓ ✓ Review [115] 2007 ✓ ✓ ✓ Review [106] 2008 ✓ ✓ ✓ Software Engineering [146] 2008 ✓ ✓ ✓ Review [131] 2008 ✓ ✓ ✓ ✓ Review [8] 2009 ✓ ✓ ✓ Software Engineering [5] 2009 ✓ ✓ ✓ Automated Traffic Management Control [222] 2009 ✓ ✓ ✓ Review [231] 2009 ✓ ✓ ✓ ✓ Review [239] 2009 ✓ ✓ ✓ ✓ Security [61] 2009 ✓ ✓ ✓ Web Services [41] 2010 ✓ ✓ ✓ Management [284] 2010 ✓ ✓ ✓ Automated Traffic Management Control [236] 2010 ✓ ✓ ✓ IoT [176] 2010 ✓ ✓ ✓ IoT [91] 2010 ✓ ✓ ✓ Review [143] 2010 ✓ ✓ ✓ Web Services [217] 2011 ✓ ✓ ✓ Review [205] 2012 ✓ ✓ ✓ Control Engineering [282] 2012 ✓ ✓ ✓ Review [298] 2012 ✓ ✓ ✓ Review [48] 2013 ✓ ✓ ✓ Networking [237] 2015 ✓ ✓ ✓ Review [158] 2015 ✓ ✓ ✓ ✓ ✓ Review [66] 2015 ✓ ✓ ✓ ✓ Web Services [83] 2016 ✓ ✓ ✓ Holonic Systems [190] 2016 ✓ ✓ ✓ Cyber Physical Systems [157] 2016 ✓ ✓ ✓ ✓ IaaS [221] 2017 ✓ ✓ ✓ Software Engineering [74] 2018 ✓ ✓ ✓ UML [256] 2018 ✓ ✓ ✓ ✓ IaaS [250] 2018 ✓ ✓ ✓ ✓ Review [26] 2018 ✓ ✓ ✓ Review [84] 2018 ✓ ✓ ✓ Review [118] 2019 ✓ ✓ ✓ ✓ Mobile Systems [77] 2019 ✓ ✓ ✓ Review

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Type Applic. Contrib. Evaluation Method Reference Year General architecture New Framework Analysis Technique New Pattern Formal Criteria New Approach Case study Simulated Extension Novel Preliminary Case Study Industrial Comparison Human Subject Quantitative Unknown/None Domain [287] 2004 ✓ ✓ ✓ ✓ Load Balancing [79] 2005 ✓ ✓ ✓ Automotive [144] 2005 ✓ ✓ ✓ ✓ IoT [145] 2005 ✓ ✓ ✓ ✓ Web Services [242] 2005 ✓ ✓ ✓ IoT [262] 2006 ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [300] 2006 ✓ ✓ ✓ ✓ ✓ Mobile Systems [154] 2006 ✓ ✓ ✓ ✓ Speech Recognition [296] 2006 ✓ ✓ ✓ ✓ Security [163] 2007 ✓ ✓ ✓ ✓ Programmer [86] 2008 ✓ ✓ ✓ ✓ Computer [123] 2008 ✓ ✓ ✓ ✓ Computer [65] 2008 ✓ ✓ ✓ ✓ IoT [255] 2008 ✓ ✓ ✓ ✓ Robotics [235] 2008 ✓ ✓ ✓ ✓ Robotics [260] 2008 ✓ ✓ ✓ ✓ Networking [204] 2008 ✓ ✓ ✓ ✓ Web Services [54] 2008 ✓ ✓ ✓ ✓ Web Services [20] 2008 ✓ ✓ ✓ ✓ Web Services [29] 2009 ✓ ✓ ✓ ✓ System on Chip [47] 2009 ✓ ✓ ✓ ✓ Service-Oriented Systems [169] 2009 ✓ ✓ ✓ ✓ Service-Oriented Systems [87] 2009 ✓ ✓ ✓ ✓ Robotics [72] 2009 ✓ ✓ ✓ ✓ Web Services [238] 2010 ✓ ✓ ✓ ✓ Automotive [247] 2010 ✓ ✓ ✓ ✓ Mobile Systems [92] 2010 ✓ ✓ ✓ ✓ IoT [210] 2010 ✓ ✓ ✓ ✓ Web Services [152] 2011 ✓ ✓ ✓ ✓ IaaS [261] 2011 ✓ ✓ ✓ ✓ Networking [94] 2011 ✓ ✓ ✓ ✓ Robotics [229] 2011 ✓ ✓ ✓ ✓ Robotics [265] 2011 ✓ ✓ ✓ ✓ Other [73] 2011 ✓ ✓ ✓ ✓ Web Services [141] 2012 ✓ ✓ ✓ ✓ Networking [194] 2012 ✓ ✓ ✓ ✓ Robotics [208] 2012 ✓ ✓ ✓ ✓ Software Engineering [116] 2012 ✓ ✓ ✓ ✓ Service-Oriented Systems [69] 2012 ✓ ✓ ✓ Web Services [165] 2012 ✓ ✓ ✓ ✓ Web Services [164] 2013 ✓ ✓ ✓ ✓ UML [6] 2013 ✓ ✓ ✓ ✓ IaaS [99] 2013 ✓ ✓ ✓ ✓ Mobile Systems [267] 2013 ✓ ✓ ✓ ✓ Service-Oriented Systems [49] 2013 ✓ ✓ ✓ ✓ Web Services [228] 2013 ✓ ✓ ✓ ✓ Web Services [97] 2014 ✓ ✓ ✓ ✓ Video Encoding [297] 2014 ✓ ✓ ✓ ✓ IoT [293] 2014 ✓ ✓ ✓ ✓ Robotics [55] 2014 ✓ ✓ ✓ Web Services [51] 2014 ✓ ✓ ✓ ✓ Web Services [129] 2015 ✓ ✓ ✓ ✓ IoT [121] 2015 ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [98] 2015 ✓ ✓ ✓ ✓ Mobile Systems [104] 2015 ✓ ✓ ✓ ✓ Networking [183] 2015 ✓ ✓ ✓ Web Services [203] 2015 ✓ ✓ ✓ ✓ Web Services [40] 2016 ✓ ✓ ✓ ✓ Bio-inspired [50] 2016 ✓ ✓ ✓ ✓ Web Services [110] 2016 ✓ ✓ ✓ ✓ Cyber Physical Systems [100] 2016 ✓ ✓ ✓ ✓ IaaS [201] 2016 ✓ ✓ ✓ ✓ IaaS [240] 2016 ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [218] 2016 ✓ ✓ ✓ ✓ Mobile Systems [280] 2016 ✓ ✓ ✓ ✓ Service-Oriented Systems [225] 2017 ✓ ✓ ✓ ✓ System on Chip [188] 2017 ✓ ✓ ✓ ✓ Automotive [230] 2017 ✓ ✓ ✓ ✓ IaaS [70] 2017 ✓ ✓ ✓ ✓ IoT [195] 2018 ✓ ✓ ✓ ✓ Clonal Plasticity [161] 2018 ✓ ✓ ✓ ✓ Retroﬁtting systems with self-adaptation [276] 2018 ✓ ✓ ✓ ✓ Cyber Physical Systems [226] 2018 ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [182] 2018 ✓ ✓ ✓ ✓ Networking [46] 2018 ✓ ✓ ✓ ✓ Security [269] 2018 ✓ ✓ ✓ ✓ Web Services [111] 2019 ✓ ✓ ✓ ✓ Cyber Physical Systems [246] 2019 ✓ ✓ ✓ ✓ IoT [44] 2019 ✓ ✓ ✓ ✓ IoT 15

TABLE 23 Empirical

Monitoring Adaptation Adaptation Evaluation Testing Strategy Strategy Applic. Technique Method Reference Year Design-time Run-time Present Present Case Study Simulated Parameter Component Preliminary Case Study Industrial Comparison Human Subject Quantitative Unknown/None Domain [28] 2004 ✓ ✓ ✓ ✓ Load Balancing [42] 2004 ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [244] 2005 ✓ ✓ ✓ ✓ ✓ Web Services [2] 2006 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Service-Oriented Systems [277] 2009 ✓ ✓ ✓ ✓ ✓ ✓ Automotive [89] 2009 ✓ ✓ ✓ ✓ ✓ Automotive [90] 2009 ✓ ✓ ✓ ✓ ✓ ✓ Automated Trafﬁc Management Control [113] 2009 ✓ ✓ ✓ ✓ Web Services [88] 2010 ✓ ✓ ✓ ✓ ✓ Water Networks [211] 2011 ✓ ✓ ✓ ✓ ✓ Intelligence Surveillance Reconnaissance [232] 2012 ✓ ✓ ✓ ✓ ✓ ✓ ✓ e-Commerce [207] 2012 ✓ ✓ ✓ ✓ ✓ ✓ Control Engineering [82] 2012 ✓ ✓ ✓ ✓ Networking [286] 2013 ✓ ✓ ✓ ✓ ✓ ✓ Automated Trafﬁc Management Control [117] 2013 ✓ ✓ ✓ ✓ ✓ Water Networks [133] 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ IaaS [124] 2015 ✓ ✓ ✓ ✓ ✓ ✓ IaaS [105] 2015 ✓ ✓ ✓ ✓ ✓ ✓ Networking [186] 2017 ✓ ✓ ✓ ✓ ✓ ✓ IaaS [68] 2017 ✓ ✓ ✓ ✓ ✓ ✓ Mobile Systems [151] 2018 ✓ ✓ Automotive [212] 2018 ✓ ✓ ✓ ✓ ✓ ✓ IaaS [103] 2018 ✓ ✓ ✓ ✓ ✓ ✓ Robotics

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