Incorporating adaptivity using learning in avionics self adaptive software: A case study

2016 ◽  
Author(s):  
Rajanikanth N Kashi ◽  
Meenakshi D'Souza ◽  
S Kumar Baghel ◽  
Nitin Kulkarni
Keyword(s):  
Adaptive Software ◽  
Self Adaptive

scholarly journals Developing Self-adaptive Software System: A Case Study

2015 ◽  
Vol 8 (4) ◽  
pp. 207-214
Author(s):  
Qingfeng Zhang ◽  
Jing Xu ◽  
Chao Zhang
Keyword(s):  
Software System ◽  
Adaptive Software ◽  
System A ◽  
Self Adaptive

scholarly journals Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 27
Author(s):  
Shereen Ismail ◽  
Kruti Shah ◽  
Hassan Reza ◽  
Ronald Marsh ◽  
Emanuel Grant
Keyword(s):  
State Of The Art ◽  
Software Systems ◽  
Adaptive Software ◽  
Sources Of Uncertainty ◽  
Self Adaptation ◽  
System Requirements ◽  
High Level ◽  
Different Sources ◽  
Self Adaptive

Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study.

scholarly journals A Holistic Self-Adaptive Software Model

2021 ◽  
Vol 12 (3) ◽  
pp. 1-10
Author(s):  
Shatha Alfar ◽  
Said Ghoul
Keyword(s):  
Added Value ◽  
Adaptive Software ◽  
Holistic Model ◽  
Car Industry ◽  
Software Model ◽  
Research Perspectives ◽  
Proposed Model ◽  
Software Lifecycle ◽  
Self Adaptive

The recent self-adaptive software systematic literature reviews stated clearly the following insufficiencies: (1) the need for a holistic self-adaptive software model to integrate its different aspects (2) The limitation of adaptations to context changes (3) The absence of a general and complete adaptations’ picture allowing its understandability, maintainability, evaluation, reuse, and variability. (4) The need for an explicit and a detailed link with resources, and (5) a usual limitation to known events. In order to metigate these insufficiencies, this paper is proposing a holistic model that integrates the operating, adaptations, and adaptations’ manager aspects. The proposed model covers all possible adaptations: operating (dealing with software functions failures), lifecycle (handling adaptations required by some software lifecycle steps), and context (facing context changes events). The presented work introduces the concept of software adaptations process integrating the specifications of all the above kind of adaptations. In fact, this work shows an explicit trace to its pure bio-inspired origin.An application of the proposed approach on a “car industry case study” demonstrated its feasibility in comparison with similar works that proved its meaningful added value and its promising research perspectives.

Reliable Composition of MAPE-K Loops in Self-Adaptive Software Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 38-54
Author(s):  
Selma Ouareth ◽  
Soufiane Boulehouache ◽  
Mazouzi Smaine
Keyword(s):  
Complex Systems ◽  
Knowledge Base ◽  
Electric Vehicle ◽  
Adaptive Systems ◽  
Design Approach ◽  
Software Systems ◽  
Adaptive Software ◽  
Control Loops ◽  
Self Adaptive

Self-adaptive systems (SASs) are controlled by autonomic manager (AM). This ensures the QoS of such complex systems within highly dynamic and unpredictable contexts. However, the massive integration of the adaptation abilities increased drastically the complexity of the AMs. To decrease the complexity and ensure correctness adaptation, scholars propose a subdivision into multi-autonomic entities (AEs) as a design approach. In such a design approach, SASs are controlled through multiple interacting AMs implementing each the well-known MAPE-K Loop. In this article, the writers propose a refinement pattern of interacting multiple MAPE-K Loops to achieve global adaptation without conflict. The authors contribute with a notation to describe the interaction of multiple MAPE-K Control Loops. To ensure the coordinated multi-attributes control, the interaction of the AEs is achieved through the knowledge base of the MAPE-K Loops. To validate the proposed pattern, a case study in the field of Electric Vehicle is presented.

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