A Reference Model as Automated Process for Software Adaptation at Runtime

RINGA: Design and verification of finite state machine for self-adaptive software at runtime

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.

The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.

The inherent nonuniqueness in modeling magnetic data can be partly reduced by adding prior information, either as mathematical constructs or simply as bounds on magnetization obtained from laboratory measurements. If a good prior model can be used as a reference model, then the quality of estimated models through an inverse approach can be greatly improved. But even though data on magnetic properties of rocks might exist, their distribution may often be quite irregular on local and regional scales, so that it is difficult to define representative classes of rock types suitable for constraining geophysical models of magnetization. We have developed a new way of constructing a reference model that varies only laterally and is confined to the part of the terrain that lies above the lowest topography in the area. To obtain this model, several estimated 2D magnetization distributions were constructed by data inversion as a function of the iteration number. Then, a suitable 2D model of the magnetization in the topography was chosen as a starting point for constructing a 3D reference model by modifying it with a vertical decay such that its average source depth was the same for all horizontal positions. The average source depth of the reference model was chosen to satisfy the average source depth obtained from analyzing the radial power spectrum of the area studied. Finally, the measured magnetic data were inverted in three dimensions using the given reference model. For a selected reference model, shallow structures indicated a better overall correlation with large remanent magnetizations measured on rock samples from the area. Throughout the entire model, the direction of magnetization was allowed to vary freely. We found that the Euclidean norm of the estimated model was reduced compared with the case where the magnetization direction was fixed.

Self-adaptation ability is particularly desirable for mission critical software (MCS). This paper proposes a fuzzy control-based approach to provide a systematic, engineering, and intuitive way for programmers to achieve software self adaptation. This approach uses fuzzy logic to handle uncertainty in software, uses natural-language style to describe self-adaptation logic, and makes control visible in software. These greatly shorten the knowledge and semantics gap between control engineering and software engineering, and reduce the realization difficulty of software self-adaptation. We illustrate our approach through the development of an adaptive MCS application in process control systems.

iContractML 2.0: A domain-specific language for modeling and deploying smart contracts onto multiple blockchain platforms

Self-adaptive software is expected to adjust itself attributes or structures at runtime in response to changes. Aiming at addressing some challenging problems such as difficult mathematically modeling software using the current control theoretical methods, we propose a novel fuzzy-control-based approach to achieve self-adaptive software, which is presented as framework of fuzzy self-adaptive software (FFSAS). In this framework, the general model, the implementation architecture, and the design methodology are put forward and discussed in detail. The fuzzy-control-based approach is evaluated with a news-website case study.

Reference models such as Parnas' four-variable model, Jackson's and Zaves' world machine model, and Gunther et al.'s WRSPM model abstractly define and relate key artifacts in requirements engineering. Such reference models are intended to serve as a frame of reference for engineers to understand and reason about the artifacts involved in requirements engineering. However, when discussing the requirements of modern systems that are developed in a hierarchical and middle-out manner, these reference models do not provide a framework in which the relationship between requirements and architecture is explicitly discussed. Conceptual clarity about this relationship is crucial since the architecture and requirements for such systems become intrinsically intertwined as the architectural choices made during development influence the requirements and vice-versa. Hence, to precisely determine the scope of specifying requirements, distinguish requirements from architecture details, reason about the requirements, and determine how the requirements are realized in the system, we argue that a requirements reference model intended as a reference for such systems must explicitly discuss the architecture - requirements relationship. To that end, we define a hierarchical reference model that formally, yet abstractly, captures the intertwined relationship between the architecture and requirements in a way that will serve the same purpose as other models, but be more suitable for modern systems where architecture and requirements co-evolve. To illustrate the concepts in this model, we use a generic patient-controlled analgesic infusion pump system as a case example.

In this research, we propose a lighting control system for environments with multiple light sources, including a natural light source and an artificial light source, based on a self–adaptive software control system. We also propose an algorithm for optimization between control devices in a multi-lighting environment, and evaluation methods for self-adaptive software in an Internet of Things environment. Based on these proposals, a simulation is carried out.

Self-adaptation endows software with the ability to adjust its behavior according to the changes in its environment. It is an effective approach to deal with dynamic environments, such as ubiquitous computing spaces. However, adaptability will significantly complicate software implementation and bring great challenges to software engineering practices. This paper presents an engineering approach for self-adaptive software, which systematically enables the reuse of software adaptation on both software architecture and development process. We propose a novel conceptual model as the foundation of our work at first, in which each software entity can imitate the reacting/joining behavior pattern of the individuals in natural adaptive systems. And then, mainly based on the enhancement of component technology, we map this conceptual model into a practical and reusable software architecture. A software factory with a model-driven process is presented as well. Several applications have been developed to validate the effectiveness and generality of our approach.

Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as “technical debt”. The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level. Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Altogether, the two case studies contributed 5–6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data—representing the technical debt of a file at a given point in time. Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

This paper presents a benchmarking of different reference models for Industry 4.0 solutions, using available alignment reports as a tool for benchmarking, a qualitative indicator to assess the appropriateness of the use of the different reference models, and an assessment using existing implementations and proposals as an initial starting point for future benchmark use cases. The main objective of the benchmark is to facilitate the adoption of reference models for the architectural definition of new digital manufacturing platforms. With this purpose, the benchmark first identifies the main synergies and complementarities of the different reference models under analysis and later performs a qualitative analysis of the relevance of the definitions they contain in the context of concrete implementations and proposals. In early stages of the definition of a new digital manufacturing platform, this is a useful start to position the proposal in the problem space spanned by the reference models and understand which aspects are really needed. The benchmarking can also be useful for the definition of new reference models for specific application domains or meta-models of reference models that aim to map features of different reference models in a common framework.KeywordsCloud manufacturingMethods and tools for interoperabilityEnterprise application integrationReference ontologies and standardization

The aim of this research was to develop a maintenance performance measurement model, based on three main steps, these being: (1) identify and screen factors affecting maintenance performance, (2) identify relevant key indicators for the measurement of maintenance performance, and (3) apply those key indicators developed as part of a manufacturing company case study. Using three reference models - the Cost of Poor Maintenance Model, the Malcolm Baldrige National Quality Award and the Context-Input-Process-Product assessment model, the first step was to identify a number of factors relevant to maintenance performance, as taken from previous research. The resulting 105 factors were a mixture of quantitative and qualitative factors, and were categorized according to the four perspectives within the Balances Scorecard, these being learning and growth, internal process, customer, and finance. A questionnaire used to screen those factors affecting maintenance performance was then distributed to stakeholders and maintenance staff at the case study manufacturer, which produces aircraft galleys and associated equipment. An analysis was conducted using descriptive statistics, and according to the study results, 20 factors were found to most affect maintenance performance. Once the factors had been identified, a second step was carried out - to identify the key maintenance performance measurement indicators, of which 6 were financial, 6 were customer related, 5 were related to the internal process and 3 indicators were linked to learning and growth. The final step was to employ the indicators obtained as part of a study of the case study manufacturer, in order to evaluate maintenance performance there. The results of the evaluation, once obtained, helped develop a framework that could be used to improve the maintenance system.

When computational process occurs in uncertain environments, the environmental changes often cause unpredictable influence on the computation. Self-adaptive software is one of the possible ways to tackle and handle this motivation. It evaluates its own behavior and surrounding environment, and adapts itself when the evaluation indicates that it is not accomplishing what the system is intended to do, or when better functionality is possible. Since one of the objectives of role models is to support object adaptation to the environment through role binding mechanism, it is expected that role modeling can be conveniently used to build self-adaptive systems. This paper provides some essential features or mechanisms of self-adaptive systems which are intended to be captured by adaptive role models. We propose Self-Epsilon model, an extension of our developed role-based model Epsilon, to combine and integrate all those features with role-based mechanisms into a single model. This paper also shows how the model can be applied to practically design and implement self-adaptive systems. We illustrate our approach through a case study of a simplified system of coordinated traffic jam monitoring used in some related work. The evaluation shows the reasonable and acceptable scalability of our model implementation in the framework.