scholarly journals Uncertainty in Self-adaptive Systems: A Research Community Perspective

2020 ◽  
Vol 15 (4) ◽  
pp. 1-36
Author(s):  
Sara M. Hezavehi ◽  
Danny Weyns ◽  
Paris Avgeriou ◽  
Radu Calinescu ◽  
Raffaela Mirandola ◽  
...  
Keyword(s):  
Adaptive Systems ◽  
Future Research ◽  
Functional Requirements ◽  
Second Stage ◽  
Uncertainty Sources ◽  
Uncertainty Handling ◽  
Reference Process ◽  
Key Characteristics ◽  
The Impact ◽  
Self Adaptive

One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community’s perception on what constitutes uncertainty in self-adaptive systems and on the key characteristics of the approaches needed to tackle uncertainty. To understand this perception and learn from it, we conducted a survey comprising two complementary stages in which we collected the views of 54 and 51 participants, respectively. In the first stage, we focused on current research and development, exploring how the concept of uncertainty is understood in the community and how uncertainty is currently handled in the engineering of self-adaptive systems. In the second stage, we focused on directions for future research to identify potential approaches to dealing with unanticipated changes and other open challenges in handling uncertainty in self-adaptive systems. The key findings of the first stage are: (a) an overview of uncertainty sources considered in self-adaptive systems, (b) an overview of existing methods used to tackle uncertainty in concrete applications, (c) insights into the impact of uncertainty on non-functional requirements, (d) insights into different opinions in the perception of uncertainty within the community and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: (a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, (b) a set of potential approaches for dealing with unanticipated change, (c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems. We anticipate that the insights on uncertainty obtained from the community and our proposed reference process will inspire valuable future research on self-adaptive systems.

scholarly journals The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective

2021 ◽  
Vol 11 (24) ◽  
pp. 11784
Author(s):  
Hashem Alyami ◽  
Mohd Nadeem ◽  
Abdullah Alharbi ◽  
Wael Alosaimi ◽  
Md Tarique Jamal Ansari ◽  
...  
Keyword(s):  
Quantum Computing ◽  
Weather Forecasting ◽  
Quantum Computer ◽  
Software Security ◽  
Future Research ◽  
Hybrid Technique ◽  
Analytic Hierarchy ◽  
Second Stage ◽  
The Impact ◽  
Hierarchy Process

The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software’s durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer.

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.

PERSA: A Requirements Specification Process for Self-Adaptive Systems Based on Fuzzy Logic and NFR-Framework

2017 ◽  
Vol 25 (01) ◽  
pp. 145-178 ◽  
Author(s):  
João Dionisio Paraiba ◽  
Luiz Eduardo G. Martins
Keyword(s):  
Fuzzy Logic ◽  
Case Studies ◽  
Adaptive Systems ◽  
Automated System ◽  
Human Intervention ◽  
Functional Requirements ◽  
Early Assessment ◽  
Wide Variability ◽  
Theoretical Proposal ◽  
Self Adaptive

Self-Adaptive Systems are able to change their behavior at runtime according to the environment where they are. This study presents an approach to specify the requirements for self-adaptive systems based on the concepts of Fuzzy Logic, which deals with factors such as ambiguity, uncertainties and vague information on the solution of problems; and NFR-Framework, which deals with the non-functional requirements which, very often, vaguely and full of uncertainties present themselves. Adaptive systems consist of (functional and non-functional) requirements, which hold the capacity to modify themselves during the runtime with little or no human intervention at all. Requirements that carry out the feature of wide variability are called adaptive requirements. PERSA (acronym from “Processo de Especificação de Requisitos Adaptativos”, in Portuguese) is reported in this work, using the Fuzzy Logic and the NFR-Framework as a basis, since both offers resources to manage uncertainties, an inherent attribute of self-adaptive systems. This process aims the approach of specification of adaptive requirements in a systematic way providing a guide to support requirements engineers. PERSA Process is settled in three mains phases subdivided into several steps. Two case studies were developed to validate it: the first deals with an automated system to prepare steaks, which needs to adapt to its several types; the second relates to a system for automation for canine diets, which must be adapted to different breeds of dogs according to their size, weight and classification. The case studies provide a first approach of the use and benefits of PERSA Process. In these studied the theoretical proposal was evaluated and discussed in order to establish the degree of understanding, the clarity of activities and the necessary adjustments to improve the proposed achievements, thus obtaining a satisfactory though early assessment which answers the purpose of specifying the requirements for self-adaptive systems.

Investment behavior of Canada egg producers

2018 ◽  
Vol 120 (1) ◽  
pp. 96-107 ◽  
Author(s):  
Lota D. Tamini ◽  
Maurice Doyon ◽  
Micheline M. Zan
Keyword(s):  
Risk Aversion ◽  
Fast Food ◽  
Egg Production ◽  
Investment Decision ◽  
Quality Data ◽  
Future Research ◽  
Programming Approach ◽  
Content Type ◽  
Uncertainty Sources ◽  
The Impact

Purpose The purpose of this paper is to document the level of risk in the Québec egg sector (conventional and specialty eggs) and analyze the optimal choices of Québec egg producers that must allocate limited resources to production of different types of eggs. Design/methodology/approach A quadratic programming approach applied to expected mean-variance models is used to analyze the impact of risk on decision to invest when the resources must be allocated to different type of production that have different risk levels. The model is calibrated using monthly data from 2009 to 2016. Findings Results indicated multiple uncertainty sources (technological, cost of production, price of eggs) that vary according to the types of eggs. Given risk aversion parameters, producer would favor production modes with the lowest producers’ price variance, which correspond to free-run eggs. Results also indicated that in response to a greater intensity of risk aversion, the course of action producers may choose is to increase the relative production of free-run eggs. Research limitations/implications The empirical limits of this research are found in the lack of quality data on producer prices and costs for specialty eggs. Future research could explore the relationship between the growing impact of egg for processing, which price is based on the US price, and its relationship with specialty eggs. Practical implications The findings of the study will be useful for policy makers and managers of eggs supply chain. This is important, given the recent announcement by Canadian’s large retailers and fast food companies to increase cage free eggs offering and, in some cases, eventually only selling these types of eggs. Originality/value This study adds to the understanding of the role of risk and uncertainty in the investment decision of egg producers and different mode of production, as well as in the development of the growing production of specialty eggs in Canada. It fills a gap in the literature regarding the impact of risk in Canadian egg production. This gap is likely explained by the perception of a lack of risk in this supply managed sector in Canada and its small size relative to other supply managed sector.

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.

Self-Compassion and Quality of Life in Adults Who Stutter

2020 ◽  
Vol 29 (4) ◽  
pp. 2097-2108
Author(s):  
Robyn L. Croft ◽  
Courtney T. Byrd
Keyword(s):  
Quality Of Life ◽  
Social Connectedness ◽  
The Self ◽  
Future Research ◽  
Self Compassion ◽  
Adults Who Stutter ◽  
And Gender ◽  
Relationship Of ◽  
The Impact

Purpose The purpose of this study was to identify levels of self-compassion in adults who do and do not stutter and to determine whether self-compassion predicts the impact of stuttering on quality of life in adults who stutter. Method Participants included 140 adults who do and do not stutter matched for age and gender. All participants completed the Self-Compassion Scale. Adults who stutter also completed the Overall Assessment of the Speaker's Experience of Stuttering. Data were analyzed for self-compassion differences between and within adults who do and do not stutter and to predict self-compassion on quality of life in adults who stutter. Results Adults who do and do not stutter exhibited no significant differences in total self-compassion, regardless of participant gender. A simple linear regression of the total self-compassion score and total Overall Assessment of the Speaker's Experience of Stuttering score showed a significant, negative linear relationship of self-compassion predicting the impact of stuttering on quality of life. Conclusions Data suggest that higher levels of self-kindness, mindfulness, and social connectedness (i.e., self-compassion) are related to reduced negative reactions to stuttering, an increased participation in daily communication situations, and an improved overall quality of life. Future research should replicate current findings and identify moderators of the self-compassion–quality of life relationship.

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