A Systematic Literature Review of Requirements Modeling and Analysis for Self-adaptive Systems

2014 ◽  
pp. 55-71 ◽  
Author(s):  
Zhuoqun Yang ◽  
Zhi Li ◽  
Zhi Jin ◽  
Yunchuan Chen
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Modeling And Analysis ◽  
Requirements Modeling ◽  
Self Adaptive

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

2021 ◽  
Author(s):  
Yong-Jun Shin ◽  
Joon-Young Bae ◽  
Doo-Hwan Bae
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Research Community ◽  
Current Understanding ◽  
Runtime Environment ◽  
Important Concern ◽  
Runtime Environments ◽  
Self Adaptive

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.

scholarly journals Applying Machine Learning in Self-adaptive Systems

2021 ◽  
Vol 15 (3) ◽  
pp. 1-37
Author(s):  
Omid Gheibi ◽  
Danny Weyns ◽  
Federico Quin
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Large Sets ◽  
Learning Techniques ◽  
Self Adaptation ◽  
Self Adaptive

Recently, we have been witnessing 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 analyzing 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 an overview is important for researchers to understand the state of the art and direct future research efforts. This article 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 Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. 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 6,709 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.

scholarly journals Towards a Requirements Specification Multi-View Framework for Self-Adaptive Systems

2015 ◽  
Author(s):  
Juan C. Muñoz-Fernández ◽  
Gabriel Tamura ◽  
Raúl Mazo ◽  
Camille Salinesi
Keyword(s):  
Adaptive Systems ◽  
Software Tool ◽  
Software Systems ◽  
Functional Requirements ◽  
Requirements Modeling ◽  
Sufficient Power ◽  
Depth Analysis ◽  
Context Variables ◽  
Difficult Challenge ◽  
Self Adaptive

The analysis of self-adaptive systems (SAS) requirements involves addressing uncertainty from several sources. Despite advances in requirements for SAS, uncertainty remains an extremely difficult challenge. In this paper, we propose REFAS, a framework to model the requirements of self-adaptive software systems. Our aim with REFAS is to address and reduce uncertainty and to provide a language with sufficient power of expression to specify the different aspects of self-adaptive systems, relative to functional and non-functional requirements. The REFAS modeling language includes concepts closely related to these kind of requirements and their fulfillment, such as context variables, claims, and soft dependencies. Specifically, the paper´s contribution is twofold. First, REFAS supports different viewpoints and concerns related to requirements modeling, with key associations between them. Moreover, the modeler can define additional models and views by exploiting the REFAS meta-modeling capability, in order to capture additional aspects contributing to reduce uncertainty. Second, REFAS promotes in-depth analysis of all of the modeled concerns with aggregation and association capabilities, especially with context variables. Furthermore, we also define a process that enforces modeling requirements, considering different aspects of uncertainty. We demonstrate the applicability of REFAS by using the VariaMos software tool, which implements the REFAS meta-model, views, and process.

Requirement Uncertainty Modeling for Service Oriented Self-Adaptive Software

2012 ◽  
Vol 433-440 ◽  
pp. 4798-4801
Author(s):  
Wei Liu ◽  
Chen Wan He ◽  
Zai Wen Feng
Keyword(s):  
Adaptive Systems ◽  
Domain Knowledge ◽  
Uncertainty Modeling ◽  
Limited Attention ◽  
Requirement Analysis ◽  
Modeling Framework ◽  
Modeling And Analysis ◽  
Service Oriented ◽  
Solving Strategy ◽  
Self Adaptive

Service-oriented software utilizes services as fundamental elements for developing applications that have the capability to autonomously modify their behavior at run-time in response to changes in their environment. While a few techniques have been developed to support the modeling and analysis of requirements for self-adaptive systems, limited attention has been paid to the description of service requirements and uncertainty in requirements of service-oriented software. In this paper, we propose a task solving strategy for requirement analysis and modeling framework as a fundamental of self-adaptation evolution. We introduce task solving strategy method for requirement analysis process; a context snapshot model to represent uncertainty in requirement with domain knowledge; goal-oriented context requirement to model user requirements and process-oriented context requirement to model service requirements; and finally, propose means-c-end analysis to relate user and service requirement with context condition.

scholarly journals PEMODELAN REQUIREMENTS DALAM MENGKONSTRUKSI PERANGKAT LUNAK SELF-ADAPTIVE

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Aradea Aradea ◽  
Iping Supriana ◽  
Kridanto Surendro
Keyword(s):  
Requirements Engineering ◽  
Adaptive Systems ◽  
Feedback Loop ◽  
Adaptive Software ◽  
Requirements Modeling ◽  
Integration Approach ◽  
Construction Of Self ◽  
Loop Feedback ◽  
Engineering Integration ◽  
Self Adaptive

[Id]Mengkonstruksi perangkat lunak self-adaptive sangat berbeda dengan mengkonstruksi perangkat lunak non self-adaptive, hal ini menuntut banyak cara yang harus ditempuh untuk mencapai tujuan tersebut. Salah satunya adalah pada tahapan pemodelan requirements. Pendekatan yang digunakan saat melakukan pemodelan requirements untuk perangkat lunak self-adaptive, tidak cukup hanya menangkap kebutuhan sesuai dengan kondisi systems-as-is. Namun kebutuhan systems-to-be yang berhubungan dengan spesifikasi perilaku, dan kemampuannya untuk menangani perubahan ketika sistem sedang berjalan, merupakan faktor penting yang harus terpenuhi. Makalah ini membahas pemodelan requirements untuk mengembangkan self-adaptive systems, dengan mengintegrasikan pendekatan goal oriented requirements engineering dan feedback loop. Diawali dengan latar belakang, kemudian menguraikan penelitian terkait, dilanjutkan dengan konsep yang diusulkan beserta contoh penerapannya, dan diakhir bahasan kami menguraikan pekerjaan untuk masa depan serta kesimpulan.Kata kunci:Requirements modeling, goal oriented requirements engineering, self-adaptive systems, feedback loop[En]Construction of self-adaptive software is very different with the construction of non-self-adaptive software, its require many ways that must be through to gain these goals. one of them is on the requirement of modelling phase. The approach that used, when conduct modelling requirement is not enough to catch the needs appropriate with as-is system condition, but the requirement of to-be systems that connected with behaviour specification and its ability to handle transformation when system running is an important factor that must be fulfilled. this paper describes requirement modelling to develop self-adaptive systems, with goal oriented engineering integration approach and loop feedback. Started with the background, then untangle related research, continued with proposed concept and its implementation example, and in the last description, we untangle conclusion and our future works.Keywords: Requirements modeling, goal oriented requirements engineering, self-adaptive systems, feedback loop

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