Self-adaptive Software Research Articles

Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation.

Enhancing Software Architecture Adaptability: A Comprehensive Evaluation Method

The field of self-adaptive software is becoming increasingly crucial because software has to adapt its behavior at runtime to keep up with dynamic and constantly evolving environments. The ability of software to modify and adjust itself is known as adaptability, which has been acknowledged as an important quality attribute. As software architecture development represents the initial stage of the design process, architectural design has emerged as a critical development activity. The degree to which the architecture can adapt to changes will be the key factor in determining the adaptability of the software ultimately released to users. Existing metrics for software architectural adaptability typically view adaptability as a positive attribute, meaning that any possibility of change in architectural elements is perceived as an improvement in adaptability. However, the application of adaptability that leads to increased costs or complexity can only be seen as a negative attribute. In other words, the side effects brought about solely for the purpose of achieving adaptability are greatly overlooked; that is, the impacts caused by adaptability exhibit both symmetry and asymmetry for software architectural adaptability. On the other hand, the existing measures of adaptability only define it from the perspective of whether a module will change, without considering changes beyond the topological structure under multiple strategies. In this paper, we propose a comprehensive assessment approach to measure software architecture adaptability. Multiple factors are considered, including the number and types of strategies employed, the cost of completing the adaptation, the extent of changes made to the architectural elements, and the overall impact of the adaptation on the architecture. An extended UML (e-UML) component diagram is presented to model software architectural changes under various types of strategies. Finally, an experiment on the znn.com software is conducted to validate the practical feasibility of our proposed method. Our approach can assist software architects in making informed decisions regarding software architecture design options to improve the adaptability and longevity of their software systems.

Advancing Smart Cyber Physical System with Self-Adaptive Software

The proposed integration of DevOps and Digital Twins presents a ground-breaking approach to enhancing the resilience and adaptability of Cyber-Physical Systems in the context of Industry 4.0 and the forthcoming Industry 5.0 era. This research explores the synergistic potential of DevOps principles in fostering collaborative and efficient development and maintenance processes for embedded systems and distributed control systems within CPS. Concurrently, the utilization of Digital Twins offers real-time monitoring and simulation capabilities, heralding a shift towards a human-centric approach to CPS operation. The emphasis on self-adaptive systems within CPS anticipates the ability to address dynamic conditions and unforeseen challenges, paving the way for the evolution of resilient and responsive industrial systems. This research explores the integration of DevOps and Digital Twins in the context of Industry 4.0 and anticipates the requirements of Industry 5.0, emphasizing the need for resilient and human-centric Cyber-Physical Systems. This study investigates the integration of DevOps and Digital Twins to enhance the resilience and adaptability of Cyber-Physical Systems in the context of Industry 4.0 and Industry 5.

A Reference Architecture Based on Reflection for Self-Adaptive Software: A Second Release

A Reference Architecture Based on Reflection for Self-Adaptive Software: A Second Release

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty

Self-adaptive software (SAS) is gaining in popularity as it can handle dynamic changes in the operational context or in itself. Time behaviors are of vital importance for SAS systems, as the self-adaptation loops bring in additional overhead time. However, early modeling and quantitative analysis of time behaviors for the SAS systems is challenging, especially under uncertainty environments. To tackle this problem, this paper proposed an approach called Timed-SAS to define, describe, analyze, and optimize the time behaviors within the SAS systems. Concretely, Timed-SAS: (1) provides a systematic definition on the deterministic time constraints, the uncertainty delay time constraints, and the time-based evaluation metrics for the SAS systems; (2) creates a set of formal modeling templates for the self-adaptation processes, the time behaviors and the uncertainty environment to consolidate design knowledge for reuse; and (3) provides a set of statistical model checking-based quantitative analysis templates to analyze and verify the self-adaptation properties and the time properties under uncertainty. To validate its effectiveness, we presented an example application and a subject-based experiment. The results demonstrated that the Timed-SAS approach can effectively reduce modeling and verification difficulties of the time behaviors, and can help to optimize the self-adaptation logic.

Прогнозирование взаимодействий в самоадаптивном программном обеспечении

Прогнозирование взаимодействий в самоадаптивном программном обеспечении

Self-Adaptive Framework With Master–Slave Architecture for Internet of Things

The Internet of Things (IoT) connects a wide range of entities and can be applied to various types of environments. In addition, IoT environments can be dynamically changed at runtime; thus, IoT systems can be deployed in various environments. To support stable operation, IoT systems must adapt to dynamic environmental changes. The self-adaptive software aims to adjust various artifacts or attributes of software to adapt the detected context by itself, and various studies have applied self-adaptive methods in IoT-related research. In this study, we proposed a self-adaptive software framework with master–slave architecture-based finite-state machine modeling. In addition, model checking is applied, which is a formal method to verify IoT systems at runtime, and a cache-based mechanism is applied to reduce the computational time required for verification. To demonstrate the efficiency of the proposed framework, an empirical evaluation was performed with several model-checking tools (RINGA, NuSMV, nuXmv, and CadenceSMV), and the results showed the efficiency of the proposed framework with the cache-based mechanism. In addition, an example application was investigated with smart greenhouse scenarios, and the application was implemented on Android and Arduino. The application was operated in physical environments, and the results showed the practical usability of the proposed framework with verification at runtime.

Testing Self-Adaptive Software With Probabilistic Guarantees on Performance Metrics: Extended and Comparative Results

This paper discusses methods to test the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approach to overcome the mentioned difficulties and provide probabilistic guarantees on the software performance. We describe the set up needed for the application of a probabilistic approach. We then discuss the traditional tools from statistics that could be applied to analyse the results, highlighting their limitations and motivating why they are unsuitable for the given problem. We propose the use of a novel tool – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the Scenario Theory</i> – to overcome said limitations. We conclude the paper with a thorough empirical evaluation of the proposed approach, using three adaptive software applications: the Tele-Assistance Service, the Self-Adaptive Video Encoder, and the Traffic Reconfiguration via Adaptive Participatory Planning. With the first, we empirically expose the trade-off between data collection and confidence in the testing campaign. With the second, we demonstrate how to compare different adaptation strategies. With the third, we discuss the role of the randomisation in the selection of test inputs. In the evaluation, we apply the scenario theory and also classical statistical tools: Monte Carlo and Extreme Value Theory. We provide a complete evaluation and a thorough comparison of the confidence and guarantees that can be given with all the approaches.

An Adaptive System for a Real-Time Matching Application

In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and manage which function with different locations and contents is most valuable to the user's experience without using computationally time-consuming optimization calculations. To address this, this paper is focusing on self-adaptive software technology. The authors built a software adaptation mechanism that can be immediately calculated online using a performance characteristic map and threshold judgment with a learning function and sequential updates. The results confirmed the effectiveness of the mechanism in an application that supports personnel exchange events.

A Holistic Self-Adaptive Software Model

A Holistic Self-Adaptive Software Model

A Holistic Self-Adaptive Software Model

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.

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

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.

Behavior Trend Analysis Method of Software Runtime Environment Elements Based on ARIMA

Behavior trend analysis method based on auto-regressive integrated moving average (ARIMA) is presented for software runtime environment elements (REEs). The history data of environment element behavior is collected to build behavior trend analysis model. Then, behavior trend of environment elements is predicted before the environment elements actually change. Thus, according to the analysis results, we can determine the corresponding adaptive strategy before the environment elements actually change, which provides a reference for the selection and implementation of active adaptive strategy for the self-adaptive software.

Разработка комплексного подхода к программной самоадаптации и универсальный метод синтеза адаптивных программных систем

Разработка комплексного подхода к программной самоадаптации и универсальный метод синтеза адаптивных программных систем

Self-Adaptive Software Systems in Contested and Resource-Constrained Environments: Overview and Challenges

Self-adaptive approaches are a promising to address the dynamic and uncertain nature of the environments where today’s complex systems operate. In particular, systems operating in military environments, during crises or under unexpected conditions need to address critical concerns related to resource sparsity and the unstable and uncertain nature of exchanged information. Despite a plethora of self-adaptive and autonomic approaches proposed in the last decade, very few have been designed for or evaluated in contested environments , where adversarial action in the communications domain leads to stale or incomplete information, or in resource-constrained environments, where resources are either limited or required by a large number of components. These conditions are where self-adaptability to aid human operators is needed the most. To better understand self-adaptation in contested and resource-constrained environments, we conducted a systematic literature review of publications over the last decade. We conduct our review through the lens of a military environment, where contention, both physical and from a resource, be it computational or communication based is at its peak. We followed the systematic literature review methodology and analysed 238 primary studies. We identified that the most frequent application domains are those where failures are frequent and costly, namely, cloud computing, web services and applications, and servers. Despite this, less than 3% of the papers considered constrained resources and stale or incomplete information and a significant focus was on developing centralised solutions instead of distributed ones throughout all papers. Very few papers (4.6%) considered environments where the information about the system components was not readily available. No papers evaluated the systems running in contested and resource-constrained environments. We present an analysis of the self-adaptive systems that consider incomplete or stale information and constrained resources, discuss their limitations and identify future areas of research. Critical research gaps include the lack of evaluation of self-adaptive approaches, including the lack of standards or formalisms to allow for the comparison of various approaches. In addition, there is a need to consider more self-* properties and non-functional requirements in order to make sure the designed system is resilient. This paper presents our review findings in detail, examines how self-adaptation happens in contested and resource-constrained environments, and discusses the identified research gaps.

Model-driven engineering and usability evaluation of self-adaptive user interfaces

Enes Yigitbas is a Senior Researcher at Paderborn University. In 2019, he got his doctoral degree in Computer Science under the advisory of Prof. Dr. Gregor Engels. During his doctorate, his research work was linked to various industrial projects in the Software Innovation Campus Paderborn (SICP). In this context, he was significantly involved in the implementation and management of various projects related to model-driven and human-centered software engineering, self-adaptive software systems, and human-computer interaction. His work on engineering adaptive user interfaces received a late-breaking results award at the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. In addition to project and teaching activities, he is actively involved in various committee activities as well as serves as a reviewer for various international conferences and journals in the areas of Software Engineering and HCI. In his thesis, supervised by Prof. Dr. Gregor Engels (Paderborn University), he focused on the engineering and usability evaluation of self-adaptive user interfaces. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically detect context changes and adapt to the current context-of-use at runtime. In the course of his thesis work, he developed a model-driven engineering approach which supports modeling, transformation, and execution of self-adaptive user interfaces. Furthermore, a novel on-the-fly usability testing solution for SAUIs has been introduced which allows to evaluate the end-user satisfaction of SAUIs by combining context monitoring together with collection of instant user feedback.

EasyModel: A Refinement-Based Modeling and Verification Approach for Self-Adaptive Software

Self-adaptive software (SAS) is gaining popularity as it can reconfigure itself in response to the dynamic changes in the operational context or itself. However, early modeling and formal analysis of SAS systems becomes increasingly difficult, as the system scale and complexity is rapidly increasing. To tackle the modeling difficulty of SAS systems, we present a refinement-based modeling and verification approach called EasyModel. EasyModel integrates the intuitive Unified Modeling Language (UML) model with the stepwise refinement Event-B model. Concretely, EasyModel: 1) creates a UML profile called AdaptML that provides an explicit description of SAS characteristics, 2) proposes a refinement modeling mechanism for SAS systems that can deal with system modeling complexity, 3) offers a model transformation approach and bridges the gap between the design model and the formal model of SAS systems, and 4) provides an efficient way to verify and guarantee the correct behaviour of SAS systems. To validate EasyModel, we present an example application and a subject-based experiment. The results demonstrate that EasyModel can effectively reduce the modeling and formal verification difficulty of SAS systems, and can incorporate the intuitive merit of UML and the correct-by-construction merit of Event-B.

Synergizing Domain Expertise With Self-Awareness in Software Systems: A Patternized Architecture Guideline

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.

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

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.