Requirements-driven design of autonomic application software

Autonomic computing systems reduce software maintenance costs and management complexity by taking on the responsibility for their configuration, optimization, healing, and protection. These tasks are accomplished by switching at runtime to a different system behaviour - the one that is more efficient, more secure, more stable, etc. - while still fulfilling the main purpose of the system. Thus, identifying the objectives of the system, analyzing alternative ways of how these objectives can be met, and designing a system that supports all or some of these alternative behaviours is a promising way to develop autonomic systems. This paper proposes the use of requirements goal models as a foundation for such software development process and demonstrates this on an example

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project Phase 5

Motivation and Context: One of the key elements of the SERC's research strategy is transforming the practice of systems engineering and associated management practices- "SE and Management Transformation (SEMT)." The Grand Challenge goal for SEMT is to transform the DoD community 's current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first ,document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise-oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060)

Evaluating Mission-Critical Self-Adaptive Software Systems: A Testing-Based Approach

Self-adaptive software is a closed-loop system that tries to manage, direct, or regulate its own behavior dynamically. Such a system aims at providing an automated and systematic approach to handling the increasing complexity of operation management. Mission-critical systems (e.g., e-business and telecommunication systems) are usually large, complex, and distributed. These systems must preserve their Quality of Service (QoS) at runtime under highly dynamic and non-deterministic conditions; therefore, they are suitable candidates for being equipped with self-adaptive capabilities. Although significant efforts have been devoted to modeling, designing, developing and deploying self-adaptive software since a decade ago, there is still a lack of well-established concrete processes for evaluating such systems. This dissertation proposes a systematic evaluation process for mission-critical self-adaptive software systems. The process is a well-defined testing approach that needs a post-mortem analysis, takes the quantified QoS requirements as inputs, and comprises two main phases: i) conducting system-level testing, and ii) evaluating QoS requirements satisfaction. The process uses Service Level Agreements (SLAs) as quantified QoS requirements, and consequently as the adaptation requirements of mission-critical systems. Adaptation requirements are specific types of requirements used to engineer self-adaptive software. Moreover, for the first phase, the dissertation discusses the uniqueness and necessity of conducting system-level load and stress testing on a self-adaptive software system, for collecting runtime QoS data. In the second phase, the process makes use of utility functions to generate a single value indicating the QoS satisfaction of the evaluated system. The dissertation mainly focuses on evaluating the performance, availability and reliability characteristics of QoS. An open source service-oriented Voice over IP (VoIP) application was selected as a case study. The VoIP application was transformed into a self-adaptive software system with various types of adaptation mechanisms. A set of empirical experiments was performed on the developed self-adaptive VoIP application, and the proposed process was adopted for evaluating the effectiveness of different adaptation mechanisms. To this end, the dissertation defines a sample SLA for the VoIP application, presents a report on the load and stress testing performed on the self-adaptive VoIP application, and presents a set of utility functions for evaluation. The experiments illustrate the validity, reliability, flexibility, and cost of the proposed evaluation process. In sum, this dissertation introduces a novel evaluation process for mission-critical self-adaptive software systems, and shows that the proposed process can help researchers to systematically evaluate their self-adaptive systems

Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)

International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)

SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today’s systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today’s systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Peer ReviewedPostprint (author's final draft

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures

Evolving Software Systems for Self-Adaptation

There is a strong synergy between the concepts of evolution and adaptation in software engineering: software adaptation refers to both the current software being adapted and to the evolution process that leads to the new adapted software. Evolution changes for the purpose of adaptation are usually made at development or compile time, and are meant to handle predictable situations in the form of software change requests. On the other hand, software may also change and adapt itself based on the changes in its environment. Such adaptive changes are usually dynamic, and are suitable for dealing with unpredictable or temporary changes in the software's operating environment. A promising solution for software adaptation is to develop self-adaptive software systems that can manage changes dynamically at runtime in a rapid and reliable way. One of the main advantages of self-adaptive software is its ability to manage the complexity that stems from highly dynamic and nondeterministic operating environments. If a self-adaptive software system has been engineered and used properly, it can greatly improve the cost-effectiveness of software change through its lifespan. However, in practice, many of the existing approaches towards self-adaptive software are rather expensive and may increase the overall system complexity, as well as subsequent future maintenance costs. This means that in many cases, self-adaptive software is not a good solution, because its development and maintenance costs are not paid off. The situation is even worse in the case of making current (legacy) systems adaptive. There are several factors that have an impact on the cost-effectiveness and usability of self-adaptive software; however the main objective of this thesis is to make a software system adaptive in a cost-effective way, while keeping the target adaptive software generic, usable, and evolvable, so as to support future changes. In order to effectively engineer and use self-adaptive software systems, in this thesis we propose a new conceptual model for identifying and specifying problem spaces in the context of self-adaptive software systems. Based on the foundations of this conceptual model, we propose a model-centric approach for engineering self-adaptive software by designing a generic adaptation framework and a supporting evolution process. This approach is particularly tailored to facilitate and simplify the process of evolving and adapting current (legacy) software towards runtime adaptivity. The conducted case studies reveal the applicability and effectiveness of this approach in bringing self-adaptive behaviour into non-adaptive applications that essentially demand adaptive behaviour to sustain

Extra Functional Properties Evaluation of Self-managed Software Systems with Formal Methods

Multitud de aplicaciones software actuales están abocadas a operar en contextos dinámicos. Estos pueden manifestarse en términos de cambios en el entorno de ejecución de la aplicación, cambios en los requisitos de la aplicación, cambios en la carga de trabajo recibida por la aplicación, o cambios en cualquiera de los elementos que la aplicación software pueda percibir y verse afectada. Además, estos contextos dinámicos no están restringidos a un dominio particular de aplicaciones sino que se pueden encontrar en múltiples dominios, tales como: sistemas empotrados, arquitecturas orientadas a servicios, clusters para computación de altas prestaciones, dispositivos móviles o software para el funcionamiento de la red. La existencia de estas características disuade a los ingenieros de desarrollar software que no sea capaz de cambiar de modo alguno su ejecución para acomodarla al contexto en el que se está ejecutando el software en cada momento. Por lo tanto, con el objetivo de que el software pueda satisfacer sus requisitos en todo momento, este debe incluir mecanismos para poder cambiar su configuración de ejecución. Además, debido a que los cambios de contexto son frecuentes y afectan a múltiples dispositivos de la aplicación, la intervención humana que cambie manualmente la configuración del software no es una solución factible. Para enfrentarse a estos desafíos, la comunidad de Ingeniería del Software ha propuesto nuevos paradigmas que posibilitan el desarrollo de software que se enfrenta a contextos cambiantes de un modo automático; por ejemplo las propuestas Autonomic Computing y Self-* Software. En tales propuestas es el propio software quien gestiona sus mecanismos para cambiar la configuración de ejecución, sin requerir por lo tanto intervención humana alguna. Un aspecto esencial del software auto-adaptativo (Self-adaptive Software es uno de los términos más generales para referirse a Self-* Software) es el de planear sus cambios o adaptaciones. Los planes de adaptación determinan tanto el modo en el que se adaptará el software como los momentos oportunos para ejecutar tales adaptaciones. Hay un gran conjunto de situaciones para las cuales la propiedad de auto- adaptación es una solución. Una de esas situaciones es la de mantener al sistema satisfaciendo sus requisitos extra funcionales, tales como la calidad de servicio (Quality of Service, QoS) y su consumo de energía. Esta tesis ha investigado esa situación mediante el uso de métodos formales. Una de las contribuciones de esta tesis es la propuesta para asentar en una arquitectura software los sistemas que son auto-adaptativos respecto a su QoS y su consumo de energía. Con este objetivo, esta parte de la investigación la guía una arquitectura de tres capas de referencia para sistemas auto-adaptativos. La bondad del uso de una arquitectura de referencia es que muestra fácilmente los nuevos desafíos en el diseño de este tipo de sistemas. Naturalmente, la planificación de la adaptación es una de las actividades consideradas en la arquitectura. Otra de las contribuciones de la tesis es la propuesta de métodos para la creación de planes de adaptación. Los métodos formales juegan un rol esencial en esta actividad, ya que posibilitan el estudio de las propiedades extra funcionales de los sistemas en diferentes configuraciones. El método formal utilizado para estos análisis es el de las redes de Petri markovianas. Una vez que se ha creado el plan de adaptación, hemos investigado la utilización de los métodos formales para la evaluación de QoS y consumo de energía de los sistemas auto-adaptativos. Por lo tanto, se ha contribuido a la comunidad de análisis de QoS con el análisis de un nuevo y particularmente complejo tipo de sistemas software. Para llevar a cabo este análisis se requiere el modelado de los cambios din·micos del contexto de ejecución, para lo que se han utilizado una variedad de métodos formales, como los Markov modulated Poisson processes para estimar los parámetros de las variaciones en la carga de trabajo recibida por la aplicación, o los hidden Markov models para predecir el estado del entorno de ejecución. Estos modelos han sido usados junto a las redes de Petri para evaluar sistemas auto-adaptativos y obtener resultados sobre su QoS y consumo de energía. El trabajo de investigación anterior sacó a la luz el hecho de que la adaptabilidad de un sistema no es una propiedad tan fácilmente cuantificable como las propiedades de QoS -por ejemplo, el tiempo de respuesta- o el consumo de energÌa. En consecuencia, se ha investigado en esa dirección y, como resultado, otra de las contribuciones de esta tesis es la propuesta de un conjunto de métricas para la cuantificación de la propiedad de adaptabilidad de sistemas basados en servicios. Para conseguir las anteriores contribuciones se realiza un uso intensivo de modelos y transformaciones de modelos; tarea para la que se han seguido las mejores prácticas en el campo de investigación de la Ingeniería orientada a modelos (Model-driven Engineering, MDE). El trabajo de investigación de esta tesis en el campo MDE ha contribuido con: el aumento de la potencia de modelado de un lenguaje de modelado de software propuesto anteriormente y métodos de transformación desde dos lenguajes de modelado de software a redes de Petri estocasticas