The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Saputri, Theresia Ratih Dewi; Lee, Seok Won

doi:10.1109/access.2020.3036037

Cited by 43 publications

(28 citation statements)

References 102 publications

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“…The research methodology consists of the following phases: Phase I -Conduct a systematic literature review (SLR): During this phase of the research study, we conducted SLR [44][45][46][47][48][49][50][51] to identify the critical success factors (CSFs) and critical risk factors (CRFs) in adapting agile methods for Phase II -Conduct a questionnaire survey: We conducted a questionnaire survey [52][53][54][55] in GSD industry. The 2 respectively, which provide an input for conduction of Phase III and Phase IV.…”

Section: Methodsmentioning

confidence: 99%

Green-Agile Maturity Model: An Evaluation Framework for Global Software Development Vendors

Rashid

Khan

et al. 2021

IEEE Access

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Agile methods are extensively adapted by software development organizations due to the competitive benefits it offers. In recent years global software development (GSD) projects practice agile methods as prominent methods to deliver the software in increments with utmost user satisfaction and affordable cost. Beside the use of agile methods, the software industry has also considered the green aspect of software, to be in line with the demands of the organizations and the world technological ecosystem. The green and sustainable feature of software should focus both the energy and resource efficiency key factors. This phenomenon of embedding the green flavor in software has emerged a new research area, green software engineering, that promises the development of eco-friendly software with minimum energy and use of less computing resources, to trim down the adverse effects on both society and environment. The principal objective of this research study is to design and develop a multi-level Green-Agile Maturity Model (GAMM) to assess the GSD vendors' agile maturity in terms of green software development. The model has been built in four phases. In phase I and II, systematic literature review (SLR) was performed to identify the success factors and risk factors that either supports or hinders the green and sustainable software development respectively by practicing the agile methods in GSD. The results have been validated from 106 relevant experts, dealing with agile and green software projects, through questionnaire survey. The experts' demographic represents 25 different countries. We also identified the industry practices through SLR and survey, to address our identified critical factors. Phase III of this research deals with development of the GAMM by categorizing the identified factors into seven Green-Agile maturity levels. A similar approach has been used in other models such as Capability Maturity Model Integration (CMMI), Implementation Maturity Model (IMM) and Software Outsourcing Vendors Readiness Model (SOVRM). In phase IV of this research, five case studies were conducted at GSD organizations, to evaluate the structure and efficacy of the GAMM, while as a major contribution, this paper presents our developed model, the GAMM, which aims to assess the green-agile maturity of the GSD vendors in terms of green and sustainable software development. INDEX TERMS Agile software development, Green and sustainable software, Green-Agile Maturity Model (GAMM), Global software development (GSD), Agile methods

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Section: Methodsmentioning

confidence: 99%

Green-Agile Maturity Model: An Evaluation Framework for Global Software Development Vendors

Rashid

Khan

et al. 2021

IEEE Access

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“…Da Silva et al investigated UML-based modeling languages for SASs [11]. Recently, applications of machine learning in SASs were surveyed by Saputri et al [12]. So far, there has been no systematic survey to organize the concepts and models of the environment of an SAS; and as far as we know, this is the first SLR to address them.…”

Section: Related Surveys On Sasmentioning

confidence: 99%

Concepts and Models of Environment in Self-Adaptive Systems: A Systematic Literature Review

Shin¹,

Bae²,

D³

2021

Preprint

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The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

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“…Finally, Saputri and Lee [76] performed a literature review that aimed at helping software engineers in proper selection of machine learning techniques based on adaptation concerns at hand. Based on the results, the authors proposed an initial taxonomy for choosing machine learning techniques in self-adaptation.…”

Section: Related Reviewsmentioning

confidence: 99%

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Gheibi,

Weyns,

Quin

2021

Preprint

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Recently, we witness a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analysing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such overview is important for researchers to understand the state of the art and direct future research efforts. This paper reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional MAPE-based feedback loop (Monitor-Analyze-Plan-Execute). The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Cited by 43 publications

References 102 publications

Green-Agile Maturity Model: An Evaluation Framework for Global Software Development Vendors

Green-Agile Maturity Model: An Evaluation Framework for Global Software Development Vendors

Concepts and Models of Environment in Self-Adaptive Systems: A Systematic Literature Review

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

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