Detecting Customer Queue “at-risk” Behaviors Based on Ethograms to Minimize Overall Service Dissatisfaction

Every service encounter corresponds to a “queue network” in which a system of waiting lines is connected to servers. We posit that each production service type (e.g., restaurant, airport) requires an adapted queue design in order to maximize attributes salient to customers (i.e., their primary elements of per-ceived value) in today’s globalized service environment. While the queues have been studied from many angles, a scientific contribution based on a human ethol-ogy approach proposing the early identification of “at-risk” behaviors to regulate queue dynamics seems to be novel. To remedy this shortcoming, the large-scale food distribution sector has been chosen for the application of a naturalistic ob-servation approach to describe in detail the behavior of customer queues. Sixteen immersion episodes were conducted in the months between May and June 2016. Using RQDA, we analyzed the immersion transcripts and identified typical cus-tomer queue behavioral patterns. Then, we developed an ethogram containing what we considered to be “at-risk” queue behaviors. This ethogram can ulti-mately be used as an anticipatory indicator in the context of feedforward man-agement controls. Feedforward control, as opposed to classical feedback con-trols, is based on the early detection of risks and the implementation of mitigation before damage occurs. While this approach requires human attention and exper-tise (which can be quite expensive and labor-intensive), there is also potential for human ethology to assist managers with supportive or complementary automa-tion. Indeed, the factual description of behaviors contained in our ethogram can easily be coded with modern technology like facial expression and body recog-nition technologies

Cloud Certification Process Validation using Formal Methods

The importance of cloud-based systems is increasing constantly as they become crucial for completing tasks in an effective and affordable manner. Yet, their use is affected by concerns about the security of the data and applications provisioned through them. Security certification provides a means of increasing confidence in such systems, by establishing that they fulfil certain security properties of interest. Certification processes involve security property assessments against specific threat models. These processes may be based on self-assessment, testing, inspection or runtime monitoring of security properties, and/or combinations of such methods (hybrid certification). One important question for all such processes is whether they actually deliver what they promise. This question is open at the moment and is the focus of our work. To address it, we have developed an approach that formalises certification processes, by translating them in the language of the Prism model-checker and uses Prism to verify properties of interest on the model of the certification process, under specific environmental assumptions

Reducing Run-Time Adaptation Space via Analysis of Possible Utility Bounds

Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the “curse of dimensionality , increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be generated by the optimal adaptation over a bounded horizon in both the best- and worst-case scenarios. It then constructs triangular possibility values using those optimized bounds to automatically compare adjacent adaptations for each configuration, keeping only the alternatives with the best range of potential results. The experimental results corroborate Thallium’s ability to significantly reduce the number of states that need to be considered with each adaptation decision, freeing up vital resources at run-time

State of runtime adaptation in service-oriented systems:what, where, when, how and right

Software as a Service reflects a ‘service-oriented’ approach to software development that is based on the notion of composing applications by discovering and invoking network-available services to accomplish some task. However, as more business organisations adopt service-oriented solutions and the demands on them grow, the problem of ensuring that the software systems can adapt fast and effectively to changing business needs, changes in their runtime environment and failures in provided services has become an increasingly important research problem. Dynamic adaptation has been proposed as a way to address the problem. However, for adaptation to be effective several other factors need to be considered. This study identifies the key factors that influence runtime adaptation in service-oriented systems (SOSs) and examines how well they are addressed in 29 adaptation approaches intended to support SOSs

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis

Arquitecturas software y herramientas de modelado para la integración del procesamiento de eventos complejos y blockchain

Blockchain es una tecnología de vanguardia que proporciona un libro de contabilidad distribuido e inmutable para almacenar transacciones, garantizando la seguridad, fiabilidad, trazabilidad, inmutabilidad y transparencia de la información. El comportamiento de la blockchain puede programarse mediante contratos inteligentes, que pueden utilizarse para especificar acuerdos entre distintas partes en tiempo de diseño y para validar el cumplimiento de las condiciones acordadas en tiempo de ejecución. Sin embargo, la implementación y gestión de contratos inteligentes es una tarea difícil no solo para expertos en la materia, sino también para desarrolladores de software, ya que requiere conocimientos avanzados de lenguajes de propósito específico como Solidity. Además, este lenguaje no soporta directamente la definición de reglas de negocio con correlación temporal de eventos y tiene restricciones en cuanto a los tipos de valores y su longitud. Las malas prácticas en la programación de contratos inteligentes pueden generar fallos o vulnerabilidades, provocando pérdidas económicas. Cualquier intento de ataque, por ejemplo, tratando de manipular los datos de la blockchain también puede causar errores y otras anomalías en la red blockchain. No obstante, detectar este tipo de situaciones de interés en tiempo real o comprobar automáticamente situaciones anómalas dentro de la red blockchain es actualmente un hándicap. Para dar respuesta a todos estos problemas, esta tesis doctoral en forma de compendio de artículos, titulada "Arquitecturas software y herramientas de modelado para la integración del procesamiento de eventos complejos y blockchain", aborda el reto de integrar las tecnologías de blockchain y el procesamiento de eventos complejos (Complex Event Processing, CEP). CEP es una tecnología potente que permite analizar y correlacionar grandes volúmenes de datos con el fin de detectar automáticamente patrones de interés en tiempo real. El objetivo principal de esta tesis doctoral es el desarrollo de arquitecturas software (centralizadas, distribuidas y contenerizadas) y herramientas de modelado para dar soporte a la integración de CEP y blockchain. Para lograr este objetivo, se han elaborado estudios del estado del arte y se han diseñado, implementado y probado arquitecturas y herramientas de modelado para la integración de CEP y blockchain. Estas herramientas permiten, entre otras cosas, la monitorización en tiempo real de los datos generados en las redes blockchain y la detección automática de anomalías en estas redes mediante patrones de eventos, la invocación automática de contratos inteligentes cuando se cumplen las condiciones de los patrones de eventos, así como la gestión de contratos inteligentes de forma amigable. Esta integración se ha logrado a través de soluciones ligeras y portables, y eliminando las engorrosas tareas de configuración a la hora de desplegarlas y utilizarlas, facilitando así la interacción y uso por parte de expertos en el dominio de aplicación, pero no en las tecnologías.Blockchain is a cutting-edge technology that provides a distributed and immutable ledger for storing transactions, ensuring security, reliability, traceability, immutability and transparency of information. The behavior of the blockchain can be programmed by means of smart contracts, which can be used to specify agreements between different parties at design time and to validate compliance with agreed conditions at runtime. However, the implementation and management of smart contracts is a difficult task not only for experts in the field, but also for software developers, as it requires advanced knowledge of specific-purpose languages such as Solidity. Moreover, this language does not directly support the possibility of defining business rules with temporal correlation of events and has restrictions on the types of values and their length. Bad practices in smart contract programming can generate bugs or vulnerabilities causing economic losses. Any attack attempt, for example, trying to manipulate the blockchain data can also cause errors and other anomalies in the blockchain network. Nevertheless, detecting this type of situations of interest in real time or automatically checking anomalous situations within the blockchain network is currently a handicap. To alleviate all these problems, this doctoral thesis in the form of a compendium of papers, entitled "Software architectures and modeling tools for integrating complex event processing and blockchain", addresses the challenge of integrating blockchain and Complex Event Processing (CEP) technologies. CEP is a powerful technology that allows analyzing and correlating large volumes of data with the purpose of automatically detecting patterns of interest in real time. The main objective of this doctoral thesis is the development of software architectures (centralized, distributed and containerized) and modeling tools to support the integration of CEP and blockchain. To achieve this goal, state-of-the-art studies have been carried out as well as the design, implementation and testing of architectures and modeling tools for integrating CEP and blockchain. These tools allow, among other things, the real-time monitoring of data generated in blockchain networks and the automatic detection of anomalies in these networks by matching event patterns, the automatic invocation of smart contracts when event pattern conditions are satisfied, as well as the smart contract management in a user-friendly way. This integration has been achieved through lightweight and portable solutions, and eliminating the cumbersome configuration tasks when deploying and operating them, thus facilitating the interaction and use by experts in the application domain, but not in the technologies